Hyne - Logs - Green and Dry
JH
- 1 Exploration
- 2 Modelling
- 3 Conclusions
- 4 Todo
1 Exploration
1.1 Dataset
1.1.1 Boards
library(lattice)
library(RODBC)
ch = odbcConnect('Hyne',uid='sa',pwd="password12")
mypanel = function(x,y,...) {
panel.abline(c(0,1), col='grey70', lty=2)
panel.xyplot(x,y,...)
}
B = sqlQuery(ch, "
select
SWILogNumber,
g.MOE [E.g],
g.Density [den.g],
m.swv [v.d.hitman],
h.[Density_kg/m3] [den.d.lhg],
h.[MOE.cltraw] [E.d.clt],
m.pith,
m.wane,
m.bow,
m.crook,
m.twist,
X_Dens [den.d.chh],
X_Clear [den.d.chh.clear],
X_DynE [E.d.chh.dyn],
X_AvgE [E.d.chh.avg],
X_MOE [E.d.chh.moe],
xc, yc
from (
select
flitchId,
max(pith) pith,
max(wane) wane,
avg(swv) swv,
avg(bow) bow,
avg(crook) crook,
avg(twist) twist
from manualMeas group by flitchId
) m
left join CHHBoards c on c.flitchId=m.flitchId
left join HyneDryBoards h on h.flitchId=m.flitchId
left join ecoustic g on m.flitchId=g.flitchId
left join (
select
flitchId,
max(SWILogNumber) SWILogNumber
from
boardEndImages i, barcode_image bi, boardEndBarcodes b
where flitchId is not null and i.id=bi.imageId and bi.barcodeId=b.id group by flitchId
) f on f.flitchId=m.flitchId
left join (
select FlitchID, avg(boardCentroidX_mm) xc, avg(boardCentroidY_mm) yc
from boardEndBarcodes b, boardEndImages i, barcode_image bi
where b.id=bi.barcodeId and i.id=bi.imageId
group by FlitchID
) xy on xy.FlitchID=m.flitchId
")
B$pith_wane = paste(B$pith,B$wane)
B$E.d.hitman = B$v.d^2*B$den.d.chh/1e9
# halve the SWV for ridiculosly fast ecoustic velocities
B$v.g = sqrt(B$E.g*1e9/B$den.g)
B$E.g.corrected = B$E.g
ii=!is.na(B$v.g) & B$v.g>4000
B$E.g.corrected[ii] = (B$v.g[ii]/2)^2*B$den.g[ii]/1e9
B$R = sqrt(B$xc^2 + B$yc^2)
nrow(B)## [1] 12021202 boards. 62 not associated with a SWILogNumber.
1.1.2 Logs
L = sqlQuery(ch, "
select
p.*,
s.*,
u.SWV [SWV.untrimmed],
t.SWV [SWV],
t.weight,
3.141592654*4.9/3*(rL*rL+rS*rL+rS*rS) [volume.heart]
from
(select * from logs where SWILogNumber is not null and DateAndTime>'2014-08-31 00:00:00') p
left join (select * from logs where SWILogNumber is not null and DateAndTime<'2014-08-31 00:00:00') s
on s.SWILogNumber=p.SWILogNumber
left join yardTrimmed t on p.SWILogNumber=t.SWILogNumber
left join yardUntrimmed u on p.SWILogNumber=u.SWILogNumber
left join (select
l.SWILogNumber, l.heartwoodDiameter_mm/2000 rL, s.heartwoodDiameter_mm/2000 rS
from logends l, logends s where s.SWILogNumber=l.SWILogNumber and s.logEnd='S' and l.logEnd='L'
) h on h.SWILogNumber=p.SWILogNumber
order by p.SWILogNumber")
summ <- function(x) {
return(list(
avg=mean(x, na.rm=TRUE),
p50=median(x, na.rm=TRUE),
p75=quantile(x, 0.75, na.rm=TRUE)
))
}
board.quality.measures = c('E.d.hitman','bow','crook','twist')
for (i in 1:nrow(L)) {
ii=!is.na(B$SWILogNumber) & B$SWILogNumber==L[i,"SWILogNumber"]
L[i,"nboards"]=sum(ii)
L[i,"nboards.complete"]=nrow(na.omit(B[ii,board.quality.measures]))
L[i,"E.avg"]=mean(B$E.d.clt[ii],na.rm=TRUE)
L[i,"E.avg.dyn"]=mean(B$E.d.hitman[ii],na.rm=TRUE)
L[i,"GPa10"]=sum(!is.na(B$E.d.hitman) & B$E.d.hitman>=10 & ii)/sum(ii)
L[i,"GPa8"]=sum(!is.na(B$E.d.hitman) & B$E.d.hitman>=8 & ii)/sum(ii)
L[i,"GPa6"]=sum(!is.na(B$E.d.hitman) & B$E.d.hitman>=6 & ii)/sum(ii)
L[i,"npith"]=sum(ii & (!is.na(B$pith) & B$pith=='y'))
for (measure in board.quality.measures) {
for (region in c('','inner','outer')) {
iregion = rep(TRUE,nrow(B))
if (region=='inner') {
iregion = B$R<=100
} else if (region=='outer') {
iregion = B$R>100
}
results = summ(B[!is.na(B[,measure])&ii&iregion,measure])
for (result in names(results)) {
if (region=='') {
out <- paste(measure,result,sep="_")
} else {
out <- paste(measure,result,region,sep="_")
}
L[i,out] = results[[result]]
}
}
}
}
# calibre uses 0 to indicate missing
L$velocity[L$velocity==0]=NA
L$velocity.1[L$velocity.1==0]=NA
L$hw.vfrac = L$volume.heart / L$volume
L$density = L$weight / L$volume
L$sweep.prod = L$m_sweep1*L$m_sweep2
L$sweep.prod.1 = L$m_sweep1.1*L$m_sweep2.1
L$E.gradient = L$E.d.hitman_avg_inner/L$E.d.hitman_avg_outer
#str(L)
predictors = c('SWV',
'm_volume','m_led','m_sed',
'm_a0','m_a1','m_a2','m_taper','m_waist',
'm_ovality','m_whorliness',
'm_sweep1','m_sweep2',
'weight','density',
'hw.vfrac',
'sweep.prod')1.1.3 Log End Imagery
Also grab digitized log end data:
D = sqlQuery(ch, "
select
flipbookNumber SWILogNumber,
logEnd,
e.type,
x_mm x,
y_mm y
from
LogEndDigitizationPoints p,
LogEndDigitizationEdges e,
LogEndDigitizations d
where
p.digitizationId=d.id
and e.digitizationId=d.id
and e.id=p.edgeID")# and logEnd='large'")1.2 Cleaning
Why are the shape metrics (m_*) missing for 10 logs? 9 of these logs (198,201,208,195,205,148,194,206,207) were the first put through, maybe the logselect software wasn’t running. The 10th is a log that for some reason couldn’t be matched to Royalty scanner Id (or perhaps no nin file matching that Id was available)
Hitman SWV isn’t available for the last trimmed log (223). Can we use untrimmed hitman swv?
#plot(L$SWV,L$SWV.untrimmed*1000.)
#identify(L$SWV,L$SWV.untrimmed*1000.,L$SWILogNumber)
c=rep('grey70',nrow(L))
c[L$SWILogNumber %in% c(157,183)]='red'
c[L$SWILogNumber %in% c(151)]='blue'
c[L$SWILogNumber %in% c(223)]='green'
tmp=L[,c("velocity","SWV.untrimmed","SWV","velocity.1")]
names(tmp)<-c("calibre.select","hitman.untrm","hitman.trmd","calibre.sawing")
pairs(tmp,col=c,main="Comparison of Log SWV Measures")
Three outliers (logs 151,157,183).
Trimmed SWV estimate good for logs 157, 183 (i.e. untrimmed SWV wrong).
Use hitman untrimmed estimate for log 151.
Use hitman untrimmed estimate for log 223.
TODO: re-extract hitman SWV from raw hitman data.
L$SWV[L$SWILogNumber==151] = L$SWV.untrimmed[L$SWILogNumber==151]*1000
L$SWV[L$SWILogNumber==223] = L$SWV.untrimmed[L$SWILogNumber==223]*1000Do the two measures of volume/LED/SED agree?
plot(volume ~ m_volume, L)
plot(SED ~ m_sed, L)
plot(LED ~ m_led, L)
Yep, pretty much.
Is the heartwood volume stuff sane?
xyplot(volume.heart ~ volume, L)
xyplot(I(volume.heart/volume) ~ I(weight/volume), L)
summary(lm(I(weight/volume) ~ I(volume.heart/volume), L))##
## Call:
## lm(formula = I(weight/volume) ~ I(volume.heart/volume), data = L)
##
## Residuals:
## Min 1Q Median 3Q Max
## -105.612 -23.525 -5.409 19.012 130.911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1058.725 8.902 118.93 <2e-16 ***
## I(volume.heart/volume) -520.867 34.059 -15.29 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39.78 on 121 degrees of freedom
## Multiple R-squared: 0.659, Adjusted R-squared: 0.6562
## F-statistic: 233.9 on 1 and 121 DF, p-value: < 2.2e-16Regression suggests average sapwood density of 1058 kg/m^3, with average heartwood density of 537 kg/m^3.
Compare log metrics computed at selection and processing time (i.e. before and after being trimmed to 4.9m):
par(mfcol=c(4,4))
log.metrics = c("m_volume","m_led","m_sed","m_taper","m_waist","m_ovality","m_whorliness","m_a0","m_a1","m_a2","m_sweep1","m_sweep2","sweep.prod")
for (m in log.metrics) {
plot(formula(paste(m,"~",m,".1",sep="")),L)
}
For the most part log metrics are similar before and after trimming. Where differences do occur it might be due to the loss of material or to un-repeatability. CHECK
1.3 Recovery
plot(nboards ~ m_volume, L)
identify(L$m_volume, L$nboards, L$SWILogNumber, cex=0.8)
## integer(0)Only one board recovered from log 106.
Two logs with abnormally poor recovery: 126, 192. Either these were sawn to non-90x40 products or gluing was poor and labels were lost.
Exclude these logs from further analysis.
L = L[!L$SWILogNumber%in%c(106,126,192),]1.4 Stiffness Out-turn
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$E.avg)],ordered=TRUE) ~ E.d.clt, B)
# and again but with only the best and worst and with individual boards
(worst=L$SWILogNumber[L$E.avg<quantile(L$E.avg,0.1)])## [1] 101 163 168 179 184 187 193 205 207 214 218 221(best=L$SWILogNumber[L$E.avg>quantile(L$E.avg,0.9)])## [1] 135 140 143 154 159 176 190 200 203 206 213 216bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$E.avg)],ordered=TRUE) ~ E.d.clt, B,subset=SWILogNumber %in% union(best,worst),
panel=function(...){
panel.bwplot(...)
panel.points(...,col='red')
}, main="Best and Worst 10% of Logs")
The worst (least stiff) boards are similar across all logs. The best (most stiff) boards howver range from <10 GPa to >15GPa.
Are good logs small?
plot(L$m_volume[order(L$E.avg)])
plot(L$nboards[order(L$E.avg)])
Bad logs tend to be both a little smaller and represented by fewer boards, but the effect is not strong.
1.5 Grade
Note that ‘GPax’ here denotes boards whose average MOE is x GPa or better, which is not really the same as having a GPax grade.
par(mfcol=c(3,1))
hist(L$GPa6, xlim=c(0,1), breaks=c(0:11)/10)
hist(L$GPa8, xlim=c(0,1), breaks=c(0:11)/10)
hist(L$GPa10, xlim=c(0,1), breaks=c(0:11)/10)
xyplot(jitter(L$GPa8,5)~jitter(L$GPa10,5))
Interesting that the 5 best logs if seeking GPa8 include one log that would be in the lower half if you were looking for GPa10.
par(mfcol=c(3,1))
plot(GPa6~ E.avg.dyn, L)
plot(GPa8~ E.avg.dyn, L)
plot(GPa10~ E.avg.dyn, L)
Log average MOE is not necessarily a good predictor of fraction of boards exceeding a particular MOE limit.
1.6 Marco’s Proposal Statement
How does the fraction GPa10+ vary with log quality? I.e. what is the % of GPa10+ in the worst X% of logs?
par(mfcol=c(1,1))
F=ecdf(L$GPa6)
plot(F)
quantile(L$GPa6, 0.05)## 5%
## 0.5quantile(L$GPa6, 0.10)## 10%
## 0.5821429TODO: using a model for log stiffness based on pre-sawing measures, redo this.
1.7 Dynamic v Static
Does it matter if we use Hitman instead of CLT results?
1.7.1 Using hitman and CHH density
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$E.avg.dyn)],ordered=TRUE) ~ E.d.hitman, B)
# and again but with only the best and worst and with individual boards
(worst.dyn=L$SWILogNumber[L$E.avg<quantile(L$E.avg,0.1)])## [1] 101 163 168 179 184 187 193 205 207 214 218 221(best.dyn=L$SWILogNumber[L$E.avg>quantile(L$E.avg,0.9)])## [1] 135 140 143 154 159 176 190 200 203 206 213 216bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$E.avg)],ordered=TRUE) ~ E.d.hitman, B,subset=SWILogNumber %in% union(best.dyn,worst.dyn),
panel=function(...){
panel.bwplot(...)
panel.points(...,col='red')
}, main="Best and Worst 10% of logs")
intersect(best, best.dyn)## [1] 135 140 143 154 159 176 190 200 203 206 213 216intersect(worst, worst.dyn)## [1] 101 163 168 179 184 187 193 205 207 214 218 221plot(L$E.avg,L$E.avg.dyn)
plot(rank(L$E.avg),rank(L$E.avg.dyn))
No, we get the same sets of logs as best and worst using either hitman+chh.density or clt for board MOE.
From here on use Hitman and CHH density based board and log average MOE.
1.8 Pith Boards
Do all logs have a similar number of pith-in boards?
plot(L$SWILogNumber, L$nboards, ylim=c(0,max(L$nboards)))
points(L$SWILogNumber, L$npith, col='red')
table(L$npith)##
## 0 1 2 3 4
## 5 43 58 13 1L$SWILogNumber[L$npith==0]## [1] 115 125 147 175 219Typically 1 or 2 pith boards per log. BUT 7 logs with no pith boards (either not recovered or not properly classified), this might skew which logs appear to be best and worst. One of these seven is also a worst 10% log so probably not an issue.
Is the fraction of pith boards a good predictor of log average stiffness?
xyplot(E.avg.dyn ~ I(npith/nboards), L)
No.
1.9 Board Densities
1.9.1 Different estimators of dry density
pairs(B[,c('den.d.chh','den.d.chh.clear','den.d.lhg'),])
CHH clear (X_Clear) and average (X_Dens) densities are pretty similar.
Use the latter (X_Dens) in conjunction with SWV to estimate MOE.
1.9.2 Green vs dry density
xyplot(den.d.chh ~ den.g, B, group=paste("wane =",wane), auto.key=TRUE)
Most of the incredibly high green density boards are waney. Probably the green mass is good, but the green volume
1.10 Variation with Distance-from-pith
plot(pith ~ R, B)
Wow. There are pith containing boards whose LE position is 140-160 mm from pith at LE.
1.10.1 Density
mypanel = function(...) {
panel.xyplot(...)
panel.grid(h=-1,v=-1)
panel.loess(..., col='red')
}
library(gridExtra)## Loading required package: gridlibrary(lattice)
grid.arrange(
xyplot(den.g ~ R, B, panel=mypanel, group=wane),
xyplot(den.d.chh ~ R, B, panel=mypanel, group=wane),
xyplot(den.d.lhg ~ R, B, panel=mypanel, group=wane),
ncol=3)
1.10.2 MOE
grid.arrange(
xyplot(E.g.corrected ~ R, B, panel=mypanel, group=wane),
xyplot(E.d.chh.dyn ~ R, B, panel=mypanel, group=wane),
xyplot(E.d.hitman ~ R, B, panel=mypanel, group=wane),
ncol=3)
1.10.3 Warp
grid.arrange(
xyplot(bow ~ R, B, panel=mypanel, group=wane),
xyplot(crook ~ R, B, panel=mypanel, group=wane),
xyplot(twist ~ R, B, panel=mypanel, group=wane),
ncol=3)
Most of the plots above exhibit a slope discontinuity between \(R=100\) and \(R=150\) mm.
Lets call boards with \(R<100\) ‘inner’ and those with \(R>100\) mm ‘outer’.
1.11 Intra-Log Variation
1.11.1 Radial
Plot variation of moe, den, bow, crook, twist with R within individual logs
for (p in c("den.g","den.d.chh","E.d.chh.dyn","bow","crook","twist")) {
print(
xyplot(formula(paste(p,"~ R | as.factor(SWILogNumber)")), B, group=pith_wane, pch=c(1,19,19,19), main=p)#, auto.key=TRUE)
)
}
Some very odd arrangements of pith and waney boards (e.g log 137 where a pith board has R > than a wane board!)
1.11.2 2D
Todo: plot moe, den, bow, crook, twist using glyph size/color/fill over individual log saw patterns
library(ggplot2)
ii = is.finite(B$SWILogNumber) & is.finite(B$xc) & is.finite(B$yc)
for (p in c("den.g","den.d.chh","E.d.chh.dyn","bow","crook","twist")) {
#B$sf=B[,p]/max(B[,p])
#xyplot(yc ~ xc | as.factor(SWILogNumber), aspect='iso', B, cex=B$sf, pch=19, subset=is.finite(p))#, cex=p)#, auto.key=TRUE)
# try ggplot2
B$size=B[,p]
print(
ggplot(B[is.finite(B$size) & ii,], aes(xc,yc))
+ geom_point(aes(size=sqrt(abs(size)),col=size,alpha=0.9))
+ geom_point(col='black', shape="+")
+ facet_wrap(~SWILogNumber)
+ coord_fixed() # achieves aspect='iso'
+ scale_colour_gradientn(colours=rainbow(4))
+ ggtitle(p)
)
}
# toWhich logs are well represented?
Plot board positions and digitized large end information.
xyplot(y ~ x | as.factor(SWILogNumber), group=paste(logEnd,type), D, type="l", aspect='iso',
panel=function(x,y,subscripts,...){
panel.grid(h=-1,y=-1)
log = D$SWILogNumber[subscripts][1]
ii = B$SWILogNumber==log
panel.xyplot(B$xc[ii],B$yc[ii],pch=19,col='black', cex=0.5)
panel.xyplot(x,y,subscripts=subscripts,...)
})
# and so after much scrutinising...
near.complete.sawpatterns = c(213,217,219,220,221,223,212,210,108,111,125,119,114,128,144,150,154,164,188,186,197)How is it that some boards fall outside of the large end? e.g. 103. Do I have the rotations correct?
1.12 Log SWV v average Board MOE
mypanel=function(x,y,...){
#panel.abline(c(0,1), col='grey70', lty=2)
panel.xyplot(x,y,...)
m=lm(y ~ x)
print(summary(m))
panel.abline(coef(m),col='red')
}
#xyplot(E.avg ~ SWV, L, panel=mypanel) # hitman in yard
#xyplot(E.avg ~ velocity, L, subset=velocity>0, panel=mypanel) # calibre log tool
xyplot(E.avg.dyn ~ SWV, L, panel=mypanel) # hitman in yard
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5195 -0.6276 0.0632 0.7209 2.8354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.6351108 1.4543727 -3.187 0.00184 **
## x 0.0041522 0.0004398 9.441 4.22e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.174 on 118 degrees of freedom
## Multiple R-squared: 0.4303, Adjusted R-squared: 0.4255
## F-statistic: 89.14 on 1 and 118 DF, p-value: 4.216e-16#xyplot(E.avg.dyn ~ velocity, L, subset=velocity>0, panel=mypanel) # calibre log toolUgly.
1.13 Warp
1.13.1 Per Log Distributions
bwpanel=function(...){
panel.bwplot(...)
panel.points(...,col='red', cex=0.3)
}
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$bow_p75)],ordered=TRUE) ~ bow, B, panel=bwpanel)
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$crook_p75)],ordered=TRUE) ~ crook, B, panel=bwpanel)
bwplot( factor(SWILogNumber,levels=L$SWILogNumber[order(L$twist_p75)],ordered=TRUE) ~ twist, B, panel=bwpanel)
1.13.2 Correlations
1.13.2.1 Between Log Quality Measures
log.quality.measures = c("E.avg.dyn",
"crook_avg","crook_p75","crook_avg_inner","crook_avg_outer",
"bow_avg","bow_p75","bow_avg_outer","bow_avg_inner",
"twist_avg")
pairs(L[,log.quality.measures],
lower.panel=function(x,y,...) {
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
ii=is.finite(x) & is.finite(y)
r <- cor(x[ii], y[ii])
txt <- format(c(r, 0.123456789), digits = 2)[1]
#txt <- paste0("r=", txt)
#cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = 1.8 * abs(r))
})
Average and 75th percentile bow and crook strongly correlated. Consider only average from here on.
1.13.2.2 Log Q vs Predictors
log.quality.measures = c("E.avg.dyn",
"crook_avg","crook_avg_inner","crook_avg_outer",
"bow_avg","bow_avg_outer","bow_avg_inner",
"twist_avg")
plots <- list()
for (p in predictors) {
for (l in log.quality.measures) {
plots <- c(plots, list(xyplot(L[,l] ~ L[,p], xlab=p, ylab=l)))
}
}
do.call(grid.arrange, c(plots, ncol=length(predictors)))
Twist turns out to be surprisingly predictable (see below). Is this due to correlation with stiffness?
xyplot(E.d.hitman_avg ~ twist_avg, L)
summary(lm(E.d.hitman_avg ~ twist_avg, L))##
## Call:
## lm(formula = E.d.hitman_avg ~ twist_avg, data = L)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7376 -0.9751 -0.0434 0.9902 4.0227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.06987 0.22902 43.969 < 2e-16 ***
## twist_avg -0.33020 0.06214 -5.314 5.14e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.397 on 118 degrees of freedom
## Multiple R-squared: 0.1931, Adjusted R-squared: 0.1862
## F-statistic: 28.23 on 1 and 118 DF, p-value: 5.144e-072 Modelling
2.1 Stiffness
Find the best linear model for log average MOE.
summary(m <- lm(E.avg.dyn ~ (m_volume+SWV+m_led+m_sed+m_a0+m_a1+m_a2+m_taper+m_waist+m_ovality+m_whorliness+m_sweep1+m_sweep2+weight+hw.vfrac), L, subset=!is.na(L$m_sed)))##
## Call:
## lm(formula = E.avg.dyn ~ (m_volume + SWV + m_led + m_sed + m_a0 +
## m_a1 + m_a2 + m_taper + m_waist + m_ovality + m_whorliness +
## m_sweep1 + m_sweep2 + weight + hw.vfrac), data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2509 -0.3997 -0.0257 0.4779 1.5447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.231e+00 2.312e+00 -2.695 0.008338 **
## m_volume -1.143e+01 6.716e+00 -1.701 0.092233 .
## SWV 4.732e-03 3.865e-04 12.245 < 2e-16 ***
## m_led 3.510e-02 1.543e-02 2.275 0.025159 *
## m_sed -4.762e-03 1.450e-02 -0.328 0.743307
## m_a0 -2.428e-02 2.419e-02 -1.004 0.318169
## m_a1 1.139e-01 1.612e-01 0.707 0.481588
## m_a2 2.172e-01 1.638e-01 1.327 0.187840
## m_taper -7.032e-01 7.591e-01 -0.926 0.356639
## m_waist -8.948e+00 1.100e+01 -0.814 0.417784
## m_ovality 7.253e+01 1.144e+02 0.634 0.527511
## m_whorliness -3.975e-01 3.351e-01 -1.186 0.238467
## m_sweep1 -1.266e-02 1.347e-01 -0.094 0.925286
## m_sweep2 -6.431e-02 9.161e-01 -0.070 0.944189
## weight 9.605e-03 5.118e-03 1.877 0.063664 .
## hw.vfrac -4.964e+00 1.297e+00 -3.829 0.000232 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.794 on 94 degrees of freedom
## Multiple R-squared: 0.7536, Adjusted R-squared: 0.7143
## F-statistic: 19.16 on 15 and 94 DF, p-value: < 2.2e-16summary(m.best <- step(m, direction="both"))## Start: AIC=-36.04
## E.avg.dyn ~ (m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + m_sweep1 +
## m_sweep2 + weight + hw.vfrac)
##
## Df Sum of Sq RSS AIC
## - m_sweep2 1 0.003 59.263 -38.035
## - m_sweep1 1 0.006 59.265 -38.030
## - m_sed 1 0.068 59.328 -37.914
## - m_ovality 1 0.254 59.513 -37.571
## - m_a1 1 0.315 59.575 -37.458
## - m_waist 1 0.418 59.677 -37.268
## - m_taper 1 0.541 59.801 -37.041
## - m_a0 1 0.635 59.895 -36.868
## - m_whorliness 1 0.887 60.147 -36.405
## <none> 59.260 -36.040
## - m_a2 1 1.110 60.369 -36.000
## - m_volume 1 1.824 61.084 -34.705
## - weight 1 2.220 61.480 -33.994
## - m_led 1 3.264 62.524 -32.143
## - hw.vfrac 1 9.243 68.503 -22.096
## - SWV 1 94.526 153.786 66.859
##
## Step: AIC=-38.03
## E.avg.dyn ~ m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + m_sweep1 +
## weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_sweep1 1 0.009 59.272 -40.017
## - m_sed 1 0.074 59.337 -39.897
## - m_ovality 1 0.253 59.516 -39.565
## - m_a1 1 0.337 59.599 -39.412
## - m_waist 1 0.424 59.687 -39.250
## - m_taper 1 0.571 59.834 -38.980
## - m_a0 1 0.643 59.906 -38.847
## - m_whorliness 1 1.004 60.267 -38.186
## <none> 59.263 -38.035
## - m_a2 1 1.192 60.455 -37.844
## - m_volume 1 2.063 61.326 -36.270
## + m_sweep2 1 0.003 59.260 -36.040
## - weight 1 2.380 61.643 -35.703
## - m_led 1 3.261 62.524 -34.143
## - hw.vfrac 1 9.462 68.725 -23.740
## - SWV 1 99.132 158.395 68.107
##
## Step: AIC=-40.02
## E.avg.dyn ~ m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_sed 1 0.078 59.350 -41.872
## - m_ovality 1 0.255 59.527 -41.546
## - m_a1 1 0.331 59.603 -41.404
## - m_waist 1 0.420 59.692 -41.241
## - m_taper 1 0.565 59.837 -40.975
## - m_a0 1 0.643 59.915 -40.831
## - m_whorliness 1 1.025 60.298 -40.131
## <none> 59.272 -40.017
## - m_a2 1 1.183 60.455 -39.844
## - m_volume 1 2.063 61.335 -38.253
## + m_sweep1 1 0.009 59.263 -38.035
## + m_sweep2 1 0.007 59.265 -38.030
## - weight 1 2.378 61.650 -37.690
## - m_led 1 3.252 62.524 -36.142
## - hw.vfrac 1 9.546 68.819 -25.591
## - SWV 1 100.231 159.503 66.874
##
## Step: AIC=-41.87
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a1 + m_a2 + m_taper +
## m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_ovality 1 0.251 59.601 -43.408
## - m_a1 1 0.309 59.660 -43.301
## - m_waist 1 0.415 59.765 -43.106
## - m_taper 1 0.537 59.887 -42.882
## - m_whorliness 1 0.985 60.336 -42.062
## <none> 59.350 -41.872
## - m_a2 1 1.143 60.493 -41.775
## - m_a0 1 1.316 60.666 -41.460
## - m_volume 1 2.012 61.363 -40.205
## + m_sed 1 0.078 59.272 -40.017
## + m_sweep2 1 0.016 59.334 -39.902
## + m_sweep1 1 0.014 59.337 -39.897
## - weight 1 2.301 61.651 -39.688
## - m_led 1 3.257 62.607 -37.996
## - hw.vfrac 1 10.161 69.512 -26.488
## - SWV 1 100.259 159.610 64.948
##
## Step: AIC=-43.41
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a1 + m_a2 + m_taper +
## m_waist + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_a1 1 0.321 59.923 -44.817
## - m_waist 1 0.426 60.027 -44.625
## - m_taper 1 0.557 60.158 -44.385
## <none> 59.601 -43.408
## - m_a2 1 1.176 60.777 -43.259
## - m_whorliness 1 1.245 60.847 -43.134
## - m_a0 1 1.364 60.965 -42.920
## + m_ovality 1 0.251 59.350 -41.872
## - m_volume 1 2.101 61.702 -41.598
## + m_sed 1 0.074 59.527 -41.546
## + m_sweep2 1 0.016 59.586 -41.437
## + m_sweep1 1 0.015 59.586 -41.436
## - weight 1 2.257 61.858 -41.319
## - m_led 1 3.592 63.194 -38.970
## - hw.vfrac 1 10.097 69.698 -28.194
## - SWV 1 100.477 160.079 63.270
##
## Step: AIC=-44.82
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_waist + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_waist 1 0.860 60.783 -45.248
## - m_a2 1 0.939 60.862 -45.106
## <none> 59.923 -44.817
## - m_whorliness 1 1.184 61.107 -44.664
## - m_a0 1 1.593 61.515 -43.931
## - m_volume 1 1.807 61.730 -43.548
## + m_a1 1 0.321 59.601 -43.408
## + m_ovality 1 0.263 59.660 -43.301
## - weight 1 2.006 61.929 -43.195
## + m_sed 1 0.053 59.870 -42.913
## + m_sweep2 1 0.041 59.882 -42.892
## + m_sweep1 1 0.008 59.915 -42.831
## - m_taper 1 2.456 62.379 -42.398
## - m_led 1 3.751 63.673 -40.139
## - hw.vfrac 1 10.857 70.780 -28.499
## - SWV 1 104.379 164.302 64.135
##
## Step: AIC=-45.25
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## <none> 60.783 -45.248
## - m_whorliness 1 1.216 61.999 -45.069
## + m_waist 1 0.860 59.923 -44.817
## + m_a1 1 0.756 60.027 -44.625
## - m_volume 1 1.750 62.533 -44.126
## + m_ovality 1 0.289 60.494 -43.773
## - m_a2 1 1.987 62.770 -43.710
## + m_sweep2 1 0.089 60.694 -43.410
## - weight 1 2.188 62.971 -43.359
## + m_sed 1 0.035 60.748 -43.312
## + m_sweep1 1 0.001 60.783 -43.249
## - m_a0 1 2.385 63.168 -43.015
## - m_taper 1 3.191 63.974 -41.621
## - m_led 1 4.962 65.745 -38.617
## - hw.vfrac 1 10.426 71.210 -29.834
## - SWV 1 104.472 165.255 62.771##
## Call:
## lm(formula = E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 +
## m_taper + m_whorliness + weight + hw.vfrac, data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2810 -0.3804 0.0230 0.5119 1.6595
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.139e+00 2.112e+00 -2.906 0.0045 **
## m_volume -1.009e+01 5.944e+00 -1.697 0.0929 .
## SWV 4.780e-03 3.646e-04 13.110 < 2e-16 ***
## m_led 4.137e-02 1.448e-02 2.857 0.0052 **
## m_a0 -3.677e-02 1.857e-02 -1.981 0.0504 .
## m_a2 5.867e-03 3.244e-03 1.808 0.0736 .
## m_taper -1.921e-01 8.384e-02 -2.291 0.0241 *
## m_whorliness -4.325e-01 3.058e-01 -1.414 0.1603
## weight 8.878e-03 4.679e-03 1.897 0.0607 .
## hw.vfrac -5.030e+00 1.215e+00 -4.142 7.22e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7796 on 100 degrees of freedom
## Multiple R-squared: 0.7472, Adjusted R-squared: 0.7245
## F-statistic: 32.85 on 9 and 100 DF, p-value: < 2.2e-16m.best$anova## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 NA NA 94 59.25986 -36.04030
## 2 - m_sweep2 1 0.003106191 95 59.26296 -38.03454
## 3 - m_sweep1 1 0.009235583 96 59.27220 -40.01740
## 4 - m_sed 1 0.078248157 97 59.35045 -41.87228
## 5 - m_ovality 1 0.250911162 98 59.60136 -43.40822
## 6 - m_a1 1 0.321454593 99 59.92281 -44.81654
## 7 - m_waist 1 0.860445923 100 60.78326 -45.24825m.best <- step(m, direction="backward")## Start: AIC=-36.04
## E.avg.dyn ~ (m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + m_sweep1 +
## m_sweep2 + weight + hw.vfrac)
##
## Df Sum of Sq RSS AIC
## - m_sweep2 1 0.003 59.263 -38.035
## - m_sweep1 1 0.006 59.265 -38.030
## - m_sed 1 0.068 59.328 -37.914
## - m_ovality 1 0.254 59.513 -37.571
## - m_a1 1 0.315 59.575 -37.458
## - m_waist 1 0.418 59.677 -37.268
## - m_taper 1 0.541 59.801 -37.041
## - m_a0 1 0.635 59.895 -36.868
## - m_whorliness 1 0.887 60.147 -36.405
## <none> 59.260 -36.040
## - m_a2 1 1.110 60.369 -36.000
## - m_volume 1 1.824 61.084 -34.705
## - weight 1 2.220 61.480 -33.994
## - m_led 1 3.264 62.524 -32.143
## - hw.vfrac 1 9.243 68.503 -22.096
## - SWV 1 94.526 153.786 66.859
##
## Step: AIC=-38.03
## E.avg.dyn ~ m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + m_sweep1 +
## weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_sweep1 1 0.009 59.272 -40.017
## - m_sed 1 0.074 59.337 -39.897
## - m_ovality 1 0.253 59.516 -39.565
## - m_a1 1 0.337 59.599 -39.412
## - m_waist 1 0.424 59.687 -39.250
## - m_taper 1 0.571 59.834 -38.980
## - m_a0 1 0.643 59.906 -38.847
## - m_whorliness 1 1.004 60.267 -38.186
## <none> 59.263 -38.035
## - m_a2 1 1.192 60.455 -37.844
## - m_volume 1 2.063 61.326 -36.270
## - weight 1 2.380 61.643 -35.703
## - m_led 1 3.261 62.524 -34.143
## - hw.vfrac 1 9.462 68.725 -23.740
## - SWV 1 99.132 158.395 68.107
##
## Step: AIC=-40.02
## E.avg.dyn ~ m_volume + SWV + m_led + m_sed + m_a0 + m_a1 + m_a2 +
## m_taper + m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_sed 1 0.078 59.350 -41.872
## - m_ovality 1 0.255 59.527 -41.546
## - m_a1 1 0.331 59.603 -41.404
## - m_waist 1 0.420 59.692 -41.241
## - m_taper 1 0.565 59.837 -40.975
## - m_a0 1 0.643 59.915 -40.831
## - m_whorliness 1 1.025 60.298 -40.131
## <none> 59.272 -40.017
## - m_a2 1 1.183 60.455 -39.844
## - m_volume 1 2.063 61.335 -38.253
## - weight 1 2.378 61.650 -37.690
## - m_led 1 3.252 62.524 -36.142
## - hw.vfrac 1 9.546 68.819 -25.591
## - SWV 1 100.231 159.503 66.874
##
## Step: AIC=-41.87
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a1 + m_a2 + m_taper +
## m_waist + m_ovality + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_ovality 1 0.251 59.601 -43.408
## - m_a1 1 0.309 59.660 -43.301
## - m_waist 1 0.415 59.765 -43.106
## - m_taper 1 0.537 59.887 -42.882
## - m_whorliness 1 0.985 60.336 -42.062
## <none> 59.350 -41.872
## - m_a2 1 1.143 60.493 -41.775
## - m_a0 1 1.316 60.666 -41.460
## - m_volume 1 2.012 61.363 -40.205
## - weight 1 2.301 61.651 -39.688
## - m_led 1 3.257 62.607 -37.996
## - hw.vfrac 1 10.161 69.512 -26.488
## - SWV 1 100.259 159.610 64.948
##
## Step: AIC=-43.41
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a1 + m_a2 + m_taper +
## m_waist + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_a1 1 0.321 59.923 -44.817
## - m_waist 1 0.426 60.027 -44.625
## - m_taper 1 0.557 60.158 -44.385
## <none> 59.601 -43.408
## - m_a2 1 1.176 60.777 -43.259
## - m_whorliness 1 1.245 60.847 -43.134
## - m_a0 1 1.364 60.965 -42.920
## - m_volume 1 2.101 61.702 -41.598
## - weight 1 2.257 61.858 -41.319
## - m_led 1 3.592 63.194 -38.970
## - hw.vfrac 1 10.097 69.698 -28.194
## - SWV 1 100.477 160.079 63.270
##
## Step: AIC=-44.82
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_waist + m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## - m_waist 1 0.860 60.783 -45.248
## - m_a2 1 0.939 60.862 -45.106
## <none> 59.923 -44.817
## - m_whorliness 1 1.184 61.107 -44.664
## - m_a0 1 1.593 61.515 -43.931
## - m_volume 1 1.807 61.730 -43.548
## - weight 1 2.006 61.929 -43.195
## - m_taper 1 2.456 62.379 -42.398
## - m_led 1 3.751 63.673 -40.139
## - hw.vfrac 1 10.857 70.780 -28.499
## - SWV 1 104.379 164.302 64.135
##
## Step: AIC=-45.25
## E.avg.dyn ~ m_volume + SWV + m_led + m_a0 + m_a2 + m_taper +
## m_whorliness + weight + hw.vfrac
##
## Df Sum of Sq RSS AIC
## <none> 60.783 -45.248
## - m_whorliness 1 1.216 61.999 -45.069
## - m_volume 1 1.750 62.533 -44.126
## - m_a2 1 1.987 62.770 -43.710
## - weight 1 2.188 62.971 -43.359
## - m_a0 1 2.385 63.168 -43.015
## - m_taper 1 3.191 63.974 -41.621
## - m_led 1 4.962 65.745 -38.617
## - hw.vfrac 1 10.426 71.210 -29.834
## - SWV 1 104.472 165.255 62.771summary(m.best <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (SWV+m_volume+m_led+m_sed+m_a0+m_a1+m_a2+m_taper+m_waist+m_ovality+m_whorliness+m_sweep1+m_sweep2+volume+weight+density+hw.vfrac)))## Start: AIC=26.03
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + density 1 60.264 74.123 -37.424
## + hw.vfrac 1 52.694 81.692 -26.728
## + weight 1 16.594 117.792 13.529
## + m_led 1 10.528 123.858 19.052
## + m_a0 1 9.871 124.515 19.634
## + m_volume 1 9.739 124.648 19.751
## + volume 1 9.636 124.750 19.842
## + m_sed 1 9.588 124.798 19.884
## + m_a2 1 8.758 125.628 20.613
## + m_waist 1 8.613 125.773 20.740
## + m_whorliness 1 7.234 127.152 21.939
## + m_a1 1 7.069 127.318 22.083
## + m_sweep2 1 2.987 131.400 25.554
## <none> 134.386 26.026
## + m_taper 1 0.740 133.646 27.418
## + m_ovality 1 0.495 133.891 27.620
## + m_sweep1 1 0.483 133.903 27.630
##
## Step: AIC=-37.42
## E.avg.dyn ~ SWV + density
##
## Df Sum of Sq RSS AIC
## + m_a2 1 4.9358 69.187 -43.004
## + m_waist 1 4.8878 69.235 -42.927
## + m_a1 1 4.5433 69.579 -42.381
## + m_led 1 4.0844 70.038 -41.658
## + m_a0 1 3.8873 70.235 -41.349
## + m_volume 1 3.6608 70.462 -40.995
## + volume 1 3.6094 70.513 -40.915
## + m_sed 1 3.6069 70.516 -40.911
## + weight 1 3.6037 70.519 -40.906
## + hw.vfrac 1 2.9957 71.127 -39.962
## + m_whorliness 1 2.5171 71.606 -39.224
## <none> 74.123 -37.424
## + m_ovality 1 0.3689 73.754 -35.972
## + m_taper 1 0.1187 74.004 -35.600
## + m_sweep2 1 0.0253 74.097 -35.461
## + m_sweep1 1 0.0001 74.123 -35.424
##
## Step: AIC=-43
## E.avg.dyn ~ SWV + density + m_a2
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 4.9345 64.252 -49.143
## + m_whorliness 1 2.5644 66.622 -45.158
## + m_volume 1 1.7599 67.427 -43.838
## + weight 1 1.7294 67.457 -43.788
## + volume 1 1.7288 67.458 -43.787
## + m_a0 1 1.7184 67.468 -43.770
## + m_sed 1 1.6642 67.523 -43.682
## + m_led 1 1.6173 67.569 -43.606
## <none> 69.187 -43.004
## + m_ovality 1 0.5706 68.616 -41.915
## + m_waist 1 0.1792 69.008 -41.289
## + m_sweep1 1 0.1372 69.050 -41.222
## + m_taper 1 0.0946 69.092 -41.154
## + m_a1 1 0.0890 69.098 -41.145
## + m_sweep2 1 0.0438 69.143 -41.073
##
## Step: AIC=-49.14
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac
##
## Df Sum of Sq RSS AIC
## + m_led 1 1.47227 62.780 -49.693
## + m_a0 1 1.43202 62.820 -49.622
## + m_sed 1 1.41226 62.840 -49.588
## + m_whorliness 1 1.40172 62.851 -49.569
## + m_volume 1 1.35472 62.898 -49.487
## + volume 1 1.29532 62.957 -49.383
## + weight 1 1.26876 62.984 -49.337
## <none> 64.252 -49.143
## + m_ovality 1 0.90857 63.344 -48.709
## + m_waist 1 0.80130 63.451 -48.523
## + m_sweep2 1 0.19505 64.057 -47.477
## + m_taper 1 0.06756 64.185 -47.259
## + m_a1 1 0.05706 64.195 -47.241
## + m_sweep1 1 0.00290 64.249 -47.148
##
## Step: AIC=-49.69
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led
##
## Df Sum of Sq RSS AIC
## + m_whorliness 1 2.13914 60.641 -51.506
## <none> 62.780 -49.693
## + m_waist 1 0.81443 61.966 -49.129
## + m_ovality 1 0.64675 62.133 -48.832
## + m_taper 1 0.48004 62.300 -48.537
## + m_a1 1 0.45018 62.330 -48.484
## + m_sweep2 1 0.16922 62.611 -47.990
## + volume 1 0.09674 62.683 -47.862
## + weight 1 0.08944 62.691 -47.850
## + m_volume 1 0.02707 62.753 -47.740
## + m_a0 1 0.00299 62.777 -47.698
## + m_sweep1 1 0.00170 62.778 -47.696
## + m_sed 1 0.00088 62.779 -47.694
##
## Step: AIC=-51.51
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness
##
## Df Sum of Sq RSS AIC
## <none> 60.641 -51.506
## + weight 1 0.96063 59.680 -51.263
## + volume 1 0.93030 59.711 -51.207
## + m_waist 1 0.77991 59.861 -50.930
## + m_volume 1 0.59059 60.050 -50.583
## + m_ovality 1 0.34718 60.294 -50.138
## + m_sed 1 0.27563 60.365 -50.007
## + m_a0 1 0.20815 60.433 -49.884
## + m_taper 1 0.03885 60.602 -49.577
## + m_a1 1 0.03030 60.611 -49.561
## + m_sweep1 1 0.02083 60.620 -49.544
## + m_sweep2 1 0.00325 60.638 -49.512##
## Call:
## lm(formula = E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led +
## m_whorliness, data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6602 -0.4242 0.0010 0.4909 1.6757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.284e+01 2.340e+00 -5.486 2.96e-07 ***
## SWV 4.923e-03 3.376e-04 14.583 < 2e-16 ***
## density 6.447e-03 1.890e-03 3.412 0.000924 ***
## m_a2 6.458e-03 2.759e-03 2.340 0.021205 *
## hw.vfrac -3.054e+00 1.275e+00 -2.394 0.018475 *
## m_led 2.272e-03 1.172e-03 1.937 0.055445 .
## m_whorliness -5.163e-01 2.709e-01 -1.906 0.059419 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7673 on 103 degrees of freedom
## Multiple R-squared: 0.7478, Adjusted R-squared: 0.7331
## F-statistic: 50.91 on 6 and 103 DF, p-value: < 2.2e-16summary(m.best.interactions <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (SWV+m_volume+m_led+m_sed+m_a0+m_a1+m_a2+m_taper+m_waist+m_ovality+m_whorliness+m_sweep1+m_sweep2+weight+density+hw.vfrac)^2))## Start: AIC=26.03
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + density 1 60.264 74.123 -37.424
## + hw.vfrac 1 52.694 81.692 -26.728
## + weight 1 16.594 117.792 13.529
## + m_led 1 10.528 123.858 19.052
## + m_a0 1 9.871 124.515 19.634
## + m_volume 1 9.739 124.648 19.751
## + m_sed 1 9.588 124.798 19.884
## + m_a2 1 8.758 125.628 20.613
## + m_waist 1 8.613 125.773 20.740
## + m_whorliness 1 7.234 127.152 21.939
## + m_a1 1 7.069 127.318 22.083
## + m_sweep2 1 2.987 131.400 25.554
## <none> 134.386 26.026
## + m_taper 1 0.740 133.646 27.418
## + m_ovality 1 0.495 133.891 27.620
## + m_sweep1 1 0.483 133.903 27.630
##
## Step: AIC=-37.42
## E.avg.dyn ~ SWV + density
##
## Df Sum of Sq RSS AIC
## + m_a2 1 4.9358 69.187 -43.004
## + m_waist 1 4.8878 69.235 -42.927
## + m_a1 1 4.5433 69.579 -42.381
## + m_led 1 4.0844 70.038 -41.658
## + m_a0 1 3.8873 70.235 -41.349
## + m_volume 1 3.6608 70.462 -40.995
## + m_sed 1 3.6069 70.516 -40.911
## + weight 1 3.6037 70.519 -40.906
## + hw.vfrac 1 2.9957 71.127 -39.962
## + m_whorliness 1 2.5171 71.606 -39.224
## <none> 74.123 -37.424
## + m_ovality 1 0.3689 73.754 -35.972
## + SWV:density 1 0.3505 73.772 -35.945
## + m_taper 1 0.1187 74.004 -35.600
## + m_sweep2 1 0.0253 74.097 -35.461
## + m_sweep1 1 0.0001 74.123 -35.424
##
## Step: AIC=-43
## E.avg.dyn ~ SWV + density + m_a2
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 4.9345 64.252 -49.143
## + m_whorliness 1 2.5644 66.622 -45.158
## + m_a2:density 1 2.0225 67.164 -44.267
## + m_volume 1 1.7599 67.427 -43.838
## + weight 1 1.7294 67.457 -43.788
## + m_a0 1 1.7184 67.468 -43.770
## + SWV:m_a2 1 1.6697 67.517 -43.691
## + m_sed 1 1.6642 67.523 -43.682
## + m_led 1 1.6173 67.569 -43.606
## <none> 69.187 -43.004
## + m_ovality 1 0.5706 68.616 -41.915
## + SWV:density 1 0.2701 68.917 -41.434
## + m_waist 1 0.1792 69.008 -41.289
## + m_sweep1 1 0.1372 69.050 -41.222
## + m_taper 1 0.0946 69.092 -41.154
## + m_a1 1 0.0890 69.098 -41.145
## + m_sweep2 1 0.0438 69.143 -41.073
##
## Step: AIC=-49.14
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac
##
## Df Sum of Sq RSS AIC
## + m_led 1 1.47227 62.780 -49.693
## + m_a0 1 1.43202 62.820 -49.622
## + m_sed 1 1.41226 62.840 -49.588
## + m_whorliness 1 1.40172 62.851 -49.569
## + density:hw.vfrac 1 1.37324 62.879 -49.519
## + m_volume 1 1.35472 62.898 -49.487
## + weight 1 1.26876 62.984 -49.337
## <none> 64.252 -49.143
## + SWV:m_a2 1 1.15660 63.096 -49.141
## + m_a2:density 1 1.12201 63.130 -49.081
## + m_ovality 1 0.90857 63.344 -48.709
## + m_waist 1 0.80130 63.451 -48.523
## + m_a2:hw.vfrac 1 0.58861 63.664 -48.155
## + SWV:density 1 0.41774 63.835 -47.860
## + SWV:hw.vfrac 1 0.23513 64.017 -47.546
## + m_sweep2 1 0.19505 64.057 -47.477
## + m_taper 1 0.06756 64.185 -47.259
## + m_a1 1 0.05706 64.195 -47.241
## + m_sweep1 1 0.00290 64.249 -47.148
##
## Step: AIC=-49.69
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led
##
## Df Sum of Sq RSS AIC
## + m_whorliness 1 2.13914 60.641 -51.506
## + SWV:m_a2 1 1.27611 61.504 -49.952
## <none> 62.780 -49.693
## + m_a2:density 1 1.12595 61.654 -49.683
## + density:hw.vfrac 1 1.11724 61.663 -49.668
## + m_led:m_a2 1 0.95369 61.826 -49.377
## + m_waist 1 0.81443 61.966 -49.129
## + m_ovality 1 0.64675 62.133 -48.832
## + m_a2:hw.vfrac 1 0.58417 62.196 -48.721
## + m_taper 1 0.48004 62.300 -48.537
## + m_a1 1 0.45018 62.330 -48.484
## + SWV:density 1 0.33635 62.444 -48.284
## + m_sweep2 1 0.16922 62.611 -47.990
## + m_led:hw.vfrac 1 0.15670 62.623 -47.968
## + SWV:hw.vfrac 1 0.15313 62.627 -47.961
## + weight 1 0.08944 62.691 -47.850
## + m_led:density 1 0.03609 62.744 -47.756
## + m_volume 1 0.02707 62.753 -47.740
## + m_a0 1 0.00299 62.777 -47.698
## + m_sweep1 1 0.00170 62.778 -47.696
## + SWV:m_led 1 0.00153 62.779 -47.695
## + m_sed 1 0.00088 62.779 -47.694
##
## Step: AIC=-51.51
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness
##
## Df Sum of Sq RSS AIC
## + density:hw.vfrac 1 1.49936 59.142 -52.260
## + SWV:m_whorliness 1 1.22896 59.412 -51.758
## <none> 60.641 -51.506
## + weight 1 0.96063 59.680 -51.263
## + m_whorliness:density 1 0.87079 59.770 -51.097
## + m_led:m_a2 1 0.78211 59.859 -50.934
## + m_waist 1 0.77991 59.861 -50.930
## + m_a2:density 1 0.76656 59.874 -50.906
## + SWV:m_a2 1 0.75462 59.886 -50.884
## + m_led:hw.vfrac 1 0.60165 60.039 -50.603
## + m_volume 1 0.59059 60.050 -50.583
## + SWV:density 1 0.50910 60.132 -50.434
## + m_ovality 1 0.34718 60.294 -50.138
## + m_led:density 1 0.32360 60.317 -50.095
## + m_whorliness:hw.vfrac 1 0.30171 60.339 -50.055
## + m_a2:hw.vfrac 1 0.27653 60.364 -50.009
## + m_sed 1 0.27563 60.365 -50.007
## + SWV:hw.vfrac 1 0.22164 60.419 -49.909
## + m_a0 1 0.20815 60.433 -49.884
## + SWV:m_led 1 0.10778 60.533 -49.702
## + m_led:m_whorliness 1 0.04617 60.595 -49.590
## + m_taper 1 0.03885 60.602 -49.577
## + m_a1 1 0.03030 60.611 -49.561
## + m_sweep1 1 0.02083 60.620 -49.544
## + m_sweep2 1 0.00325 60.638 -49.512
## + m_a2:m_whorliness 1 0.00149 60.639 -49.509
##
## Step: AIC=-52.26
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness +
## density:hw.vfrac
##
## Df Sum of Sq RSS AIC
## + SWV:m_whorliness 1 1.15777 57.984 -52.435
## <none> 59.142 -52.260
## + m_whorliness:density 1 0.90783 58.234 -51.962
## + SWV:m_a2 1 0.84639 58.295 -51.846
## + m_a2:density 1 0.68383 58.458 -51.539
## + m_waist 1 0.66992 58.472 -51.513
## + weight 1 0.55690 58.585 -51.301
## + m_led:m_a2 1 0.51149 58.630 -51.216
## + m_a2:hw.vfrac 1 0.37774 58.764 -50.965
## + m_volume 1 0.35970 58.782 -50.931
## + m_whorliness:hw.vfrac 1 0.26549 58.876 -50.755
## + SWV:density 1 0.22212 58.919 -50.674
## + m_led:hw.vfrac 1 0.20949 58.932 -50.650
## + m_led:m_whorliness 1 0.17610 58.965 -50.588
## + m_ovality 1 0.12766 59.014 -50.498
## + m_led:density 1 0.11891 59.023 -50.481
## + m_sed 1 0.11643 59.025 -50.477
## + SWV:m_led 1 0.10014 59.041 -50.447
## + m_a0 1 0.09159 59.050 -50.431
## + m_taper 1 0.08253 59.059 -50.414
## + m_a1 1 0.07172 59.070 -50.394
## + SWV:hw.vfrac 1 0.05669 59.085 -50.366
## + m_a2:m_whorliness 1 0.03288 59.109 -50.321
## + m_sweep1 1 0.02174 59.120 -50.301
## + m_sweep2 1 0.01381 59.128 -50.286
##
## Step: AIC=-52.43
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness +
## density:hw.vfrac + SWV:m_whorliness
##
## Df Sum of Sq RSS AIC
## + m_whorliness:density 1 1.49720 56.487 -53.312
## <none> 57.984 -52.435
## + SWV:m_a2 1 0.89475 57.089 -52.145
## + m_whorliness:hw.vfrac 1 0.84032 57.143 -52.041
## + m_led:m_whorliness 1 0.77212 57.212 -51.909
## + m_a2:density 1 0.64721 57.337 -51.670
## + m_waist 1 0.62547 57.358 -51.628
## + m_led:m_a2 1 0.52423 57.460 -51.434
## + weight 1 0.47763 57.506 -51.345
## + m_volume 1 0.30792 57.676 -51.021
## + m_a2:hw.vfrac 1 0.28443 57.699 -50.976
## + SWV:m_led 1 0.20816 57.776 -50.830
## + m_led:hw.vfrac 1 0.17852 57.805 -50.774
## + SWV:density 1 0.14938 57.834 -50.719
## + m_taper 1 0.12375 57.860 -50.670
## + m_led:density 1 0.10879 57.875 -50.641
## + m_a1 1 0.10701 57.877 -50.638
## + m_sed 1 0.09566 57.888 -50.616
## + m_a0 1 0.06186 57.922 -50.552
## + m_sweep1 1 0.06015 57.924 -50.549
## + m_ovality 1 0.04156 57.942 -50.514
## + SWV:hw.vfrac 1 0.01105 57.973 -50.456
## + m_sweep2 1 0.00495 57.979 -50.444
## + m_a2:m_whorliness 1 0.00000 57.984 -50.435
##
## Step: AIC=-53.31
## E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness +
## density:hw.vfrac + SWV:m_whorliness + density:m_whorliness
##
## Df Sum of Sq RSS AIC
## <none> 56.487 -53.312
## + m_waist 1 0.86143 55.625 -53.003
## + m_led:m_whorliness 1 0.83548 55.651 -52.952
## + m_a2:density 1 0.74261 55.744 -52.768
## + m_led:m_a2 1 0.74104 55.746 -52.765
## + SWV:m_a2 1 0.73913 55.747 -52.761
## + weight 1 0.68257 55.804 -52.650
## + m_volume 1 0.51930 55.967 -52.328
## + m_a2:hw.vfrac 1 0.38666 56.100 -52.068
## + SWV:m_led 1 0.33145 56.155 -51.960
## + m_led:hw.vfrac 1 0.13984 56.347 -51.585
## + m_a0 1 0.12005 56.367 -51.546
## + m_sed 1 0.09013 56.396 -51.488
## + m_ovality 1 0.07139 56.415 -51.452
## + m_sweep1 1 0.03801 56.449 -51.387
## + m_taper 1 0.03715 56.449 -51.385
## + m_a1 1 0.02751 56.459 -51.366
## + m_a2:m_whorliness 1 0.02057 56.466 -51.353
## + m_led:density 1 0.01795 56.469 -51.347
## + SWV:hw.vfrac 1 0.01778 56.469 -51.347
## + m_whorliness:hw.vfrac 1 0.00806 56.479 -51.328
## + m_sweep2 1 0.00759 56.479 -51.327
## + SWV:density 1 0.00442 56.482 -51.321##
## Call:
## lm(formula = E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led +
## m_whorliness + density:hw.vfrac + SWV:m_whorliness + density:m_whorliness,
## data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2100 -0.4475 0.0329 0.5336 1.7058
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -22.666570 6.807015 -3.330 0.00122 **
## SWV 0.006674 0.001090 6.123 1.82e-08 ***
## density 0.010810 0.005302 2.039 0.04410 *
## m_a2 0.006598 0.002792 2.363 0.02005 *
## hw.vfrac -13.093606 6.238219 -2.099 0.03834 *
## m_led 0.002497 0.001176 2.124 0.03613 *
## m_whorliness 14.691219 7.154645 2.053 0.04265 *
## density:hw.vfrac 0.011036 0.006869 1.607 0.11130
## SWV:m_whorliness -0.002043 0.001161 -1.759 0.08169 .
## density:m_whorliness -0.009240 0.005675 -1.628 0.10666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7516 on 100 degrees of freedom
## Multiple R-squared: 0.7651, Adjusted R-squared: 0.744
## F-statistic: 36.19 on 9 and 100 DF, p-value: < 2.2e-16#
m.best.current <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (m_volume+volume+SWV))## Start: AIC=26.03
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + m_volume 1 9.7385 124.65 19.751
## + volume 1 9.6358 124.75 19.842
## <none> 134.39 26.026
##
## Step: AIC=19.75
## E.avg.dyn ~ SWV + m_volume
##
## Df Sum of Sq RSS AIC
## <none> 124.65 19.751
## + volume 1 0.2187 124.43 21.558m.best.withWeight <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (m_volume+volume+SWV+weight+density+hw.vfrac))## Start: AIC=26.03
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + density 1 60.264 74.123 -37.424
## + hw.vfrac 1 52.694 81.692 -26.728
## + weight 1 16.594 117.792 13.529
## + m_volume 1 9.739 124.648 19.751
## + volume 1 9.636 124.750 19.842
## <none> 134.386 26.026
##
## Step: AIC=-37.42
## E.avg.dyn ~ SWV + density
##
## Df Sum of Sq RSS AIC
## + m_volume 1 3.6608 70.462 -40.995
## + volume 1 3.6094 70.513 -40.915
## + weight 1 3.6037 70.519 -40.906
## + hw.vfrac 1 2.9957 71.127 -39.962
## <none> 74.123 -37.424
##
## Step: AIC=-41
## E.avg.dyn ~ SWV + density + m_volume
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 2.91738 67.544 -43.646
## <none> 70.462 -40.995
## + volume 1 0.14912 70.313 -39.228
## + weight 1 0.02702 70.435 -39.037
##
## Step: AIC=-43.65
## E.avg.dyn ~ SWV + density + m_volume + hw.vfrac
##
## Df Sum of Sq RSS AIC
## <none> 67.544 -43.646
## + volume 1 0.54801 66.996 -42.543
## + weight 1 0.23127 67.313 -42.024# currently measurable: SWV, volume/size, shape
# with weight: SWV, volume/size, shape, weight, density
m.best.withWeight <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (m_volume+volume+SWV+weight+density+hw.vfrac))## Start: AIC=26.03
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + density 1 60.264 74.123 -37.424
## + hw.vfrac 1 52.694 81.692 -26.728
## + weight 1 16.594 117.792 13.529
## + m_volume 1 9.739 124.648 19.751
## + volume 1 9.636 124.750 19.842
## <none> 134.386 26.026
##
## Step: AIC=-37.42
## E.avg.dyn ~ SWV + density
##
## Df Sum of Sq RSS AIC
## + m_volume 1 3.6608 70.462 -40.995
## + volume 1 3.6094 70.513 -40.915
## + weight 1 3.6037 70.519 -40.906
## + hw.vfrac 1 2.9957 71.127 -39.962
## <none> 74.123 -37.424
##
## Step: AIC=-41
## E.avg.dyn ~ SWV + density + m_volume
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 2.91738 67.544 -43.646
## <none> 70.462 -40.995
## + volume 1 0.14912 70.313 -39.228
## + weight 1 0.02702 70.435 -39.037
##
## Step: AIC=-43.65
## E.avg.dyn ~ SWV + density + m_volume + hw.vfrac
##
## Df Sum of Sq RSS AIC
## <none> 67.544 -43.646
## + volume 1 0.54801 66.996 -42.543
## + weight 1 0.23127 67.313 -42.024m.best.withHW <- step(lm(E.avg.dyn ~ SWV, L, subset=!is.na(L$m_sed)), direction="forward", scope=E.avg.dyn ~ (m_volume+volume+SWV+hw.vfrac))## Start: AIC=26.03
## E.avg.dyn ~ SWV
##
## Df Sum of Sq RSS AIC
## + hw.vfrac 1 52.694 81.692 -26.728
## + m_volume 1 9.739 124.648 19.751
## + volume 1 9.636 124.750 19.842
## <none> 134.386 26.026
##
## Step: AIC=-26.73
## E.avg.dyn ~ SWV + hw.vfrac
##
## Df Sum of Sq RSS AIC
## + m_volume 1 4.6896 77.002 -31.231
## + volume 1 4.5145 77.177 -30.981
## <none> 81.692 -26.728
##
## Step: AIC=-31.23
## E.avg.dyn ~ SWV + hw.vfrac + m_volume
##
## Df Sum of Sq RSS AIC
## + volume 1 1.5404 75.462 -31.454
## <none> 77.002 -31.231
##
## Step: AIC=-31.45
## E.avg.dyn ~ SWV + hw.vfrac + m_volume + volume# with heartwood: SWV, volume/size, shape, weight, density, hw.vfrac
m.best.withWeightHW <- step(lm(E.avg.dyn ~ (SWV + density + weight + hw.vfrac), L))## Start: AIC=-35.69
## E.avg.dyn ~ (SWV + density + weight + hw.vfrac)
##
## Df Sum of Sq RSS AIC
## <none> 82.003 -35.688
## - weight 1 3.437 85.440 -32.761
## - hw.vfrac 1 5.222 87.225 -30.280
## - density 1 8.493 90.496 -25.862
## - SWV 1 170.287 252.291 97.171Plot best models for Marco:
myplot=function (m, lbl) {
print(s<-summary(m))
xyplot(m$model$E.avg.dyn ~ predict(m),
panel=function(x,y,...) {
panel.abline(c(0,1), col='grey70', lty=2)
panel.xyplot(x,y,...)},
aspect='iso',
xlab='Predicted', ylab='Actual',
main=sprintf('%s\nr^2=%0.2f',lbl,s$r.squared))
#main=lbl, sub=expression(r^2==s$r.squared))
}
library(gridExtra)
grid.arrange(myplot(m.best.current, lbl="Current (SWV + volume)"),
myplot(m.best.withWeight, lbl="With Log Weight"),
myplot(m.best.withHW, lbl="With Log end HW"),
myplot(m.best, lbl="Everything"),
nrow=1,
as.table=TRUE)
##
## Call:
## lm(formula = E.avg.dyn ~ SWV + m_volume, data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2365 -0.6130 -0.0252 0.6845 2.6291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.1681972 1.5528785 -3.972 0.000129 ***
## SWV 0.0044086 0.0004421 9.971 < 2e-16 ***
## m_volume 1.8252705 0.6312929 2.891 0.004647 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.079 on 107 degrees of freedom
## Multiple R-squared: 0.4817, Adjusted R-squared: 0.472
## F-statistic: 49.72 on 2 and 107 DF, p-value: 5.386e-16
##
##
## Call:
## lm(formula = E.avg.dyn ~ SWV + density + m_volume + hw.vfrac,
## data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8537 -0.3722 0.0763 0.4953 1.8764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.466e+01 2.309e+00 -6.347 5.68e-09 ***
## SWV 5.170e-03 3.414e-04 15.146 < 2e-16 ***
## density 7.391e-03 1.927e-03 3.834 0.000215 ***
## m_volume 1.121e+00 4.752e-01 2.360 0.020128 *
## hw.vfrac -2.720e+00 1.277e+00 -2.130 0.035542 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.802 on 105 degrees of freedom
## Multiple R-squared: 0.7191, Adjusted R-squared: 0.7084
## F-statistic: 67.21 on 4 and 105 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = E.avg.dyn ~ SWV + hw.vfrac + m_volume + volume,
## data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7326 -0.3921 -0.0176 0.4720 1.7951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.709e+00 1.240e+00 -5.412 3.97e-07 ***
## SWV 5.127e-03 3.643e-04 14.073 < 2e-16 ***
## hw.vfrac -6.761e+00 8.191e-01 -8.254 4.83e-13 ***
## m_volume 2.416e+01 1.564e+01 1.545 0.125
## volume -2.157e+01 1.474e+01 -1.464 0.146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8478 on 105 degrees of freedom
## Multiple R-squared: 0.6862, Adjusted R-squared: 0.6743
## F-statistic: 57.4 on 4 and 105 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = E.avg.dyn ~ SWV + density + m_a2 + hw.vfrac + m_led +
## m_whorliness, data = L, subset = !is.na(L$m_sed))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6602 -0.4242 0.0010 0.4909 1.6757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.284e+01 2.340e+00 -5.486 2.96e-07 ***
## SWV 4.923e-03 3.376e-04 14.583 < 2e-16 ***
## density 6.447e-03 1.890e-03 3.412 0.000924 ***
## m_a2 6.458e-03 2.759e-03 2.340 0.021205 *
## hw.vfrac -3.054e+00 1.275e+00 -2.394 0.018475 *
## m_led 2.272e-03 1.172e-03 1.937 0.055445 .
## m_whorliness -5.163e-01 2.709e-01 -1.906 0.059419 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7673 on 103 degrees of freedom
## Multiple R-squared: 0.7478, Adjusted R-squared: 0.7331
## F-statistic: 50.91 on 6 and 103 DF, p-value: < 2.2e-16Does eliminating logs with significant compression wood visible on ends lead to better models?
what % of lumber that is not from the cant has stiffness below 6GPa?
2.2 Warp
2.2.1 Best Linear Models
Use stepwise regression to select ‘best’ (in terms of AIC) linear model.
best.linear.model = function (y,LL=NULL,plot=TRUE,...) {
if (is.null(LL)) {
LL=L[,predictors]
}
LL$y = y
LL = na.omit(LL)#[complete.cases(LL),]
scope = formula(paste("y~",paste(predictors,collapse='+')))
#print(scope)
m.init <- lm(y ~ SWV, LL, ...)
#browser()
m <- step(m.init, direction="both", scope=scope, trace=0)
if (plot) {
print(xyplot(LL$y ~ predict(m),aspect='iso',
panel=function(...){
panel.abline(c(0,1),col='grey70')
panel.xyplot(...)},
main=deparse(substitute(y)),
xlab='predicted', ylab='observed'))
}
return(m)
}2.2.1.1 Bow
summary(m.best.bow_avg <- best.linear.model(L$bow_avg))
##
## Call:
## lm(formula = y ~ SWV + density + m_taper + m_whorliness, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.3691 -2.2349 -0.4538 1.6654 16.0980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.501185 7.972952 6.083 1.94e-08 ***
## SWV -0.006148 0.001506 -4.083 8.70e-05 ***
## density -0.014984 0.005031 -2.978 0.0036 **
## m_taper -0.222586 0.131070 -1.698 0.0924 .
## m_whorliness -2.041574 1.258311 -1.622 0.1077
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.451 on 105 degrees of freedom
## Multiple R-squared: 0.189, Adjusted R-squared: 0.1581
## F-statistic: 6.119 on 4 and 105 DF, p-value: 0.0001824summary(m.best.bow_p50 <- best.linear.model(L$bow_p50))
##
## Call:
## lm(formula = y ~ SWV + m_whorliness + weight + m_sed, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.8776 -2.2011 -0.2556 2.1400 10.5632
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.179203 5.754269 4.028 0.000107 ***
## SWV -0.006767 0.001578 -4.287 4.03e-05 ***
## m_whorliness -3.944875 1.202708 -3.280 0.001409 **
## weight -0.038237 0.009623 -3.974 0.000130 ***
## m_sed 0.087509 0.025646 3.412 0.000916 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.541 on 105 degrees of freedom
## Multiple R-squared: 0.245, Adjusted R-squared: 0.2162
## F-statistic: 8.518 on 4 and 105 DF, p-value: 5.422e-06summary(m.best.bow_p75 <- best.linear.model(L$bow_p75))
##
## Call:
## lm(formula = y ~ SWV + m_taper + density, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.0984 -3.3291 -0.6175 3.0863 20.7276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.433708 11.481376 5.177 1.08e-06 ***
## SWV -0.007701 0.002218 -3.472 0.000748 ***
## m_taper -0.548397 0.179534 -3.055 0.002851 **
## density -0.016005 0.007296 -2.194 0.030449 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.089 on 106 degrees of freedom
## Multiple R-squared: 0.1527, Adjusted R-squared: 0.1288
## F-statistic: 6.369 on 3 and 106 DF, p-value: 0.0005193SWV, weight, density, whorliness and taper best predictors. Median bow best predicted.
2.2.1.2 Crook
summary(m.best.crook_avg <- best.linear.model(L$crook_avg))
##
## Call:
## lm(formula = y ~ density + sweep.prod + m_waist, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3170 -1.4776 -0.2643 1.0128 10.4716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.653904 2.953953 3.945 0.000144 ***
## density -0.007949 0.003093 -2.570 0.011562 *
## sweep.prod 2.111823 0.877651 2.406 0.017849 *
## m_waist -1.155524 0.645883 -1.789 0.076461 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.198 on 106 degrees of freedom
## Multiple R-squared: 0.1473, Adjusted R-squared: 0.1232
## F-statistic: 6.105 on 3 and 106 DF, p-value: 0.0007164summary(m.best.crook_p50 <- best.linear.model(L$crook_p50))
##
## Call:
## lm(formula = y ~ SWV + density + sweep.prod + m_taper, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2772 -1.3010 -0.3075 0.9454 8.3582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.8230517 4.7148280 3.780 0.000261 ***
## SWV -0.0015231 0.0008867 -1.718 0.088776 .
## density -0.0090893 0.0029451 -3.086 0.002592 **
## sweep.prod 1.9155031 0.8246375 2.323 0.022116 *
## m_taper -0.1087701 0.0709175 -1.534 0.128098
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.999 on 105 degrees of freedom
## Multiple R-squared: 0.1763, Adjusted R-squared: 0.1449
## F-statistic: 5.617 on 4 and 105 DF, p-value: 0.0003891summary(m.best.crook_p75 <- best.linear.model(L$crook_p75))
##
## Call:
## lm(formula = y ~ SWV + density + sweep.prod + m_taper, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0136 -1.8490 -0.7262 1.1790 14.3093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.164952 6.939429 4.059 9.52e-05 ***
## SWV -0.002288 0.001305 -1.753 0.082520 .
## density -0.014720 0.004335 -3.396 0.000967 ***
## sweep.prod 2.879757 1.213727 2.373 0.019481 *
## m_taper -0.150029 0.104379 -1.437 0.153590
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.942 on 105 degrees of freedom
## Multiple R-squared: 0.1925, Adjusted R-squared: 0.1618
## F-statistic: 6.258 on 4 and 105 DF, p-value: 0.000147975%ile crook best predicted. Density, sweep.prod, SWV, whorliness.
2.2.1.3 Combination Warp
Stan combined bow and crook using WPA grade limits.
combined_warp = L$crook_avg/75 + L$bow_avg/25
summary(m.best.combined_warp <- best.linear.model(combined_warp))
##
## Call:
## lm(formula = y ~ SWV + density + m_taper + m_whorliness, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33366 -0.09632 -0.01685 0.06353 0.72041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.242e+00 3.370e-01 6.654 1.33e-09 ***
## SWV -2.745e-04 6.364e-05 -4.313 3.65e-05 ***
## density -7.438e-04 2.126e-04 -3.498 0.000689 ***
## m_taper -9.980e-03 5.540e-03 -1.802 0.074491 .
## m_whorliness -8.014e-02 5.319e-02 -1.507 0.134860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1459 on 105 degrees of freedom
## Multiple R-squared: 0.2133, Adjusted R-squared: 0.1833
## F-statistic: 7.116 on 4 and 105 DF, p-value: 4.14e-05What happens if we toss a couple of outliers?
L$SWILogNumber[combined_warp>1.2] # 222## [1] 222L$SWILogNumber[combined_warp<0.2] # 103## [1] 103subset = !L$SWILogNumber%in%c(103,222)
summary(m.best.combined_warp <- best.linear.model(combined_warp[subset], LL=L[subset,predictors]))
##
## Call:
## lm(formula = y ~ SWV + m_taper + density, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23232 -0.07982 -0.00473 0.06620 0.36157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.943e+00 2.825e-01 6.877 4.69e-10 ***
## SWV -2.425e-04 5.468e-05 -4.435 2.29e-05 ***
## m_taper -1.532e-02 4.407e-03 -3.476 0.000743 ***
## density -5.608e-04 1.800e-04 -3.115 0.002377 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1245 on 104 degrees of freedom
## Multiple R-squared: 0.2276, Adjusted R-squared: 0.2053
## F-statistic: 10.22 on 3 and 104 DF, p-value: 5.923e-06A little better, but nothing spectacular.
Can a random forest model do better?
library(randomForest)## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.LL=L[,predictors]
LL$combined_warp = combined_warp
LL = na.omit(LL)
m.rf <- randomForest(combined_warp ~ ., LL, importance=TRUE, ntree=50000)
par(mfcol=c(1,1))
varImpPlot(m.rf)
print(m.rf)##
## Call:
## randomForest(formula = combined_warp ~ ., data = LL, importance = TRUE, ntree = 50000)
## Type of random forest: regression
## Number of trees: 50000
## No. of variables tried at each split: 5
##
## Mean of squared residuals: 0.02542085
## % Var explained: 1.55xyplot(LL$combined_warp ~ predict(m.rf), aspect='iso')
Lousy results, but interesting that log green density turns up as the most favoured predictor, followed by SWV, taper and ovality.
What about other variable selection approaches?
library(subselect)
LL <- L[,predictors]
for (lmeas in c('E.avg.dyn','crook_avg','crook_avg_inner','crook_avg_outer','bow_avg','twist_avg')) {
LL$y = L[,lmeas]
m.0 <- lm(y ~ ., LL)
Hmat <- lmHmat(m.0)
Eleaps <- eleaps(Hmat$mat, kmin=1, kmax=length(predictors)-1, H=Hmat$H, r=Hmat$r)
plot(Eleaps$bestvalues, type='b', main=lmeas, ylab="model goodness", xlab="number of predictors")
cat(paste("\n",lmeas,"\n"))
for (i in 1:nrow(Eleaps$bestsets)) {
cat(paste(i,': ',paste(colnames(Hmat$mat)[Eleaps$bestsets[i,1:i]],collapse=", "),"\n", sep=""))
# AIC?
}
}
##
## E.avg.dyn
## 1: SWV
## 2: SWV, density
## 3: SWV, m_a2, density
## 4: SWV, m_a2, density, hw.vfrac
## 5: SWV, m_led, m_a1, density, hw.vfrac
## 6: SWV, m_led, m_a2, m_whorliness, density, hw.vfrac
## 7: SWV, m_led, m_a1, m_whorliness, weight, density, hw.vfrac
## 8: SWV, m_led, m_a0, m_a1, m_waist, m_whorliness, density, hw.vfrac
## 9: SWV, m_led, m_a0, m_a2, m_taper, m_waist, m_whorliness, density, hw.vfrac
## 10: SWV, m_led, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, density, hw.vfrac
## 11: SWV, m_led, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac
## 12: SWV, m_volume, m_led, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac
## 13: SWV, m_volume, m_led, m_sed, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac
## 14: SWV, m_volume, m_led, m_sed, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, weight, density, hw.vfrac
## 15: SWV, m_volume, m_led, m_sed, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep2, weight, density, hw.vfrac, sweep.prod
## 16: SWV, m_volume, m_led, m_sed, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
##
## crook_avg
## 1: density
## 2: density, sweep.prod
## 3: m_waist, density, sweep.prod
## 4: m_a2, m_waist, density, sweep.prod
## 5: m_a1, m_taper, m_waist, density, sweep.prod
## 6: SWV, m_a1, m_taper, m_waist, density, sweep.prod
## 7: m_volume, m_a1, m_taper, m_waist, weight, density, sweep.prod
## 8: SWV, m_volume, m_a1, m_taper, m_waist, weight, density, sweep.prod
## 9: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, weight, density, sweep.prod
## 10: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, weight, density, sweep.prod
## 11: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, weight, density, hw.vfrac, sweep.prod
## 12: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 13: SWV, m_volume, m_sed, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 14: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 15: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 16: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep2, weight, density, hw.vfrac, sweep.prod
##
## crook_avg_inner
## 1: density
## 2: m_volume, density
## 3: m_led, m_waist, density
## 4: SWV, m_sed, m_waist, density
## 5: SWV, m_sed, m_a2, m_waist, density
## 6: m_sed, m_a1, m_taper, m_waist, density, sweep.prod
## 7: SWV, m_sed, m_a1, m_taper, m_waist, density, sweep.prod
## 8: SWV, m_sed, m_a1, m_taper, m_waist, m_ovality, density, sweep.prod
## 9: SWV, m_sed, m_a1, m_taper, m_waist, m_ovality, weight, density, sweep.prod
## 10: SWV, m_sed, m_a1, m_taper, m_waist, m_ovality, weight, density, hw.vfrac, sweep.prod
## 11: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, weight, density, hw.vfrac, sweep.prod
## 12: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 13: SWV, m_volume, m_sed, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, hw.vfrac, sweep.prod
## 14: SWV, m_volume, m_sed, m_a1, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
## 15: SWV, m_volume, m_sed, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
## 16: SWV, m_volume, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
##
## crook_avg_outer
## 1: SWV
## 2: SWV, m_waist
## 3: SWV, m_a2, m_waist
## 4: SWV, m_a1, m_taper, m_waist
## 5: SWV, m_a1, m_taper, m_waist, m_sweep1
## 6: SWV, m_a1, m_taper, m_waist, m_sweep1, hw.vfrac
## 7: SWV, m_a1, m_taper, m_waist, m_sweep2, hw.vfrac, sweep.prod
## 8: SWV, m_a1, m_taper, m_waist, m_sweep2, weight, hw.vfrac, sweep.prod
## 9: SWV, m_sed, m_a0, m_a1, m_taper, m_waist, m_sweep2, hw.vfrac, sweep.prod
## 10: SWV, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_sweep2, hw.vfrac, sweep.prod
## 11: SWV, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_sweep1, m_sweep2, hw.vfrac, sweep.prod
## 12: SWV, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_sweep1, m_sweep2, weight, hw.vfrac, sweep.prod
## 13: SWV, m_volume, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_sweep1, m_sweep2, hw.vfrac, sweep.prod
## 14: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_sweep1, m_sweep2, hw.vfrac, sweep.prod
## 15: SWV, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
## 16: SWV, m_led, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
##
## bow_avg
## 1: SWV
## 2: SWV, density
## 3: SWV, m_taper, density
## 4: SWV, m_taper, m_whorliness, density
## 5: SWV, m_taper, m_ovality, m_whorliness, density
## 6: SWV, m_volume, m_a0, m_waist, m_whorliness, density
## 7: SWV, m_volume, m_sed, m_a0, m_waist, m_whorliness, density
## 8: SWV, m_volume, m_sed, m_a0, m_waist, m_ovality, m_whorliness, density
## 9: SWV, m_volume, m_sed, m_a0, m_a2, m_waist, m_ovality, m_whorliness, density
## 10: SWV, m_volume, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_whorliness, density
## 11: SWV, m_volume, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_whorliness, density, sweep.prod
## 12: SWV, m_volume, m_sed, m_a0, m_a1, m_taper, m_waist, m_ovality, m_whorliness, weight, density, sweep.prod
## 13: SWV, m_volume, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, weight, density, sweep.prod
## 14: SWV, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, sweep.prod
## 15: SWV, m_volume, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, sweep.prod
## 16: SWV, m_volume, m_sed, m_a0, m_a1, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prod
##
## twist_avg
## 1: SWV
## 2: SWV, m_led
## 3: SWV, m_led, density
## 4: SWV, m_led, m_sed, density
## 5: SWV, m_led, m_sed, m_taper, density
## 6: SWV, m_led, m_sed, m_taper, m_ovality, density
## 7: SWV, m_led, m_sed, m_taper, m_ovality, m_sweep1, density
## 8: SWV, m_volume, m_led, m_a0, m_a1, m_waist, m_ovality, density
## 9: SWV, m_volume, m_led, m_a0, m_a1, m_waist, m_ovality, m_sweep1, density
## 10: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_waist, m_ovality, m_sweep1, density
## 11: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_waist, m_ovality, m_sweep1, density, hw.vfrac
## 12: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_waist, m_ovality, m_sweep1, m_sweep2, density, sweep.prod
## 13: SWV, m_volume, m_led, m_sed, m_a0, m_a1, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, sweep.prod
## 14: SWV, m_volume, m_led, m_sed, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, sweep.prod
## 15: SWV, m_volume, m_led, m_sed, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, density, hw.vfrac, sweep.prod
## 16: SWV, m_volume, m_led, m_sed, m_a0, m_a2, m_taper, m_waist, m_ovality, m_whorliness, m_sweep1, m_sweep2, weight, density, hw.vfrac, sweep.prodNote: other R packages include bestglm, subselect, leaps, glmulti.
library(glmulti)## Loading required package: rJavalibrary(leaps)##
## Attaching package: 'leaps'
##
## The following object is masked from 'package:subselect':
##
## leapsLL <- L[,predictors]
LL$y = L$E.avg.dyn
m.0 <- glm(y ~ ., LL, family=gaussian(link="identity")) # equiv to a linear model
m.best <- glmulti(m.0, method="l", level=1) # level=1 forces no interactions## Initialization...
## TASK: Exhaustive screening of candidate set, branch-and-bound algorithm.
## [ Be sure to have package leaps installed ]
## Fitting...
## Completed.
## 225 first best models identified.# sloooooooow!From ML, May 8:
To do: * Any further models needed for warp? Why are models significantly poorer than KPP – due to compression wood outliers being removed in the KPP analysis? * Is a stiffness segregation device required on the edger? i.e. what % of lumber that is not from the cant has stiffness below 6GPa? If this is a major lets drop it. * Do we need to say anything about the mc data (NMI and aquascan)? A comment on importance of mc in warp expression and the variability we saw would be useful. * Any further implications for JNL trial?
2.3 Inner and Outer Models
2.3.1 MoE
grid.arrange(
xyplot(E.gradient ~ E.d.hitman_avg, L),
xyplot(E.gradient ~ volume, L),
ncol=2)
Reasonable range in inner:outer stiffness. Independent of overall stiffness and size.
See models below.
2.3.2 Crook
summary(m <- best.linear.model(L$crook_avg))##
## Call:
## lm(formula = y ~ density + sweep.prod + m_waist, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3170 -1.4776 -0.2643 1.0128 10.4716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.653904 2.953953 3.945 0.000144 ***
## density -0.007949 0.003093 -2.570 0.011562 *
## sweep.prod 2.111823 0.877651 2.406 0.017849 *
## m_waist -1.155524 0.645883 -1.789 0.076461 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.198 on 106 degrees of freedom
## Multiple R-squared: 0.1473, Adjusted R-squared: 0.1232
## F-statistic: 6.105 on 3 and 106 DF, p-value: 0.0007164 summary(m <- best.linear.model(L$crook_avg_inner))
##
## Call:
## lm(formula = y ~ density + m_sed + m_waist, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.288 -2.223 -0.860 1.490 13.596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.008413 4.365237 3.209 0.00176 **
## density -0.013907 0.004708 -2.954 0.00386 **
## m_sed 0.015127 0.005089 2.973 0.00366 **
## m_waist -2.070118 1.013245 -2.043 0.04353 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.328 on 106 degrees of freedom
## Multiple R-squared: 0.1365, Adjusted R-squared: 0.112
## F-statistic: 5.585 on 3 and 106 DF, p-value: 0.001352 summary(m <- best.linear.model(L$crook_avg_outer))
##
## Call:
## lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.452 -1.613 -0.563 1.139 10.283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.452262 3.664267 3.398 0.0010 **
## SWV -0.002389 0.001109 -2.154 0.0339 *
## m_waist -49.942189 32.476941 -1.538 0.1275
## m_a2 0.530931 0.354792 1.496 0.1380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.536 on 92 degrees of freedom
## Multiple R-squared: 0.1016, Adjusted R-squared: 0.07232
## F-statistic: 3.469 on 3 and 92 DF, p-value: 0.01934Best inner and outer models are both WORSE than total!
2.3.3 Bow
summary(m <- best.linear.model(L$bow_avg))##
## Call:
## lm(formula = y ~ SWV + density + m_taper + m_whorliness, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.3691 -2.2349 -0.4538 1.6654 16.0980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.501185 7.972952 6.083 1.94e-08 ***
## SWV -0.006148 0.001506 -4.083 8.70e-05 ***
## density -0.014984 0.005031 -2.978 0.0036 **
## m_taper -0.222586 0.131070 -1.698 0.0924 .
## m_whorliness -2.041574 1.258311 -1.622 0.1077
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.451 on 105 degrees of freedom
## Multiple R-squared: 0.189, Adjusted R-squared: 0.1581
## F-statistic: 6.119 on 4 and 105 DF, p-value: 0.0001824 summary(m <- best.linear.model(L$bow_avg_inner))
##
## Call:
## lm(formula = y ~ SWV + m_ovality + density + m_a1, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.8071 -3.4889 -0.9923 2.6980 17.9881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.163e+01 1.133e+01 4.559 1.4e-05 ***
## SWV -7.409e-03 2.182e-03 -3.395 0.00097 ***
## m_ovality 1.994e+03 7.068e+02 2.821 0.00573 **
## density -1.666e-02 7.329e-03 -2.273 0.02507 *
## m_a1 -3.115e-02 1.721e-02 -1.810 0.07311 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.11 on 105 degrees of freedom
## Multiple R-squared: 0.1666, Adjusted R-squared: 0.1349
## F-statistic: 5.249 on 4 and 105 DF, p-value: 0.0006807 summary(m <- best.linear.model(L$bow_avg_outer))
##
## Call:
## lm(formula = y ~ SWV + density + m_taper, data = LL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.8770 -2.9641 -0.8829 2.2955 29.8845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 76.793153 14.951333 5.136 1.56e-06 ***
## SWV -0.011094 0.002821 -3.933 0.000163 ***
## density -0.027576 0.009940 -2.774 0.006698 **
## m_taper -0.484845 0.215945 -2.245 0.027149 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.003 on 92 degrees of freedom
## Multiple R-squared: 0.1883, Adjusted R-squared: 0.1618
## F-statistic: 7.112 on 3 and 92 DF, p-value: 0.0002382.3.4 Outer c.f. Inner
Do logs with lousy inner boards also have lousy outer boards?
xyplot(crook_avg_outer ~ crook_avg_inner, L)
xyplot(bow_avg_outer ~ bow_avg_inner, L)
xyplot(twist_avg_outer ~ twist_avg_inner, L)
No. Outer and Inner pretty much uncorrelated.
2.4 Reduced Datasets
If we look only at subsets of logs that are in some way ‘good’, do we get similar prediction models?
2.4.1 Marco’s CW-free logs
Fit models only to logs Marco identified as not appearing to contain significant CW based on log end imagery.
both.ends.severe.cw = c(189, 210, 212, 222)
both.ends.moderate.cw = c(123, 135, 144, 159, 178, 188, 190, 208)
for (lmeas in log.quality.measures) {
models <- cf.full(lmeas, idx.sub=!L$SWILogNumber%in%union(both.ends.severe.cw,both.ends.moderate.cw))
}
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6602 -0.4242 0.0010 0.4909 1.6757
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.284e+01 2.340e+00 -5.486 2.96e-07 ***
SWV 4.923e-03 3.376e-04 14.583 < 2e-16 ***
density 6.447e-03 1.890e-03 3.412 0.000924 ***
m_a2 6.458e-03 2.759e-03 2.340 0.021205 *
hw.vfrac -3.054e+00 1.275e+00 -2.394 0.018475 *
m_led 2.272e-03 1.172e-03 1.937 0.055445 .
m_whorliness -5.163e-01 2.709e-01 -1.906 0.059419 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7673 on 103 degrees of freedom
Multiple R-squared: 0.7478, Adjusted R-squared: 0.7331
F-statistic: 50.91 on 6 and 103 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_volume +
m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.4238 -0.3736 0.0428 0.4779 1.5865
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.188e+01 2.325e+00 -5.110 1.74e-06 ***
SWV 4.609e-03 3.504e-04 13.153 < 2e-16 ***
density 6.731e-03 1.890e-03 3.561 0.000587 ***
m_a2 7.243e-03 2.691e-03 2.691 0.008456 **
hw.vfrac -2.465e+00 1.297e+00 -1.901 0.060439 .
m_volume 8.792e-01 4.928e-01 1.784 0.077692 .
m_whorliness -4.505e-01 2.785e-01 -1.617 0.109204
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7585 on 92 degrees of freedom
Multiple R-squared: 0.7335, Adjusted R-squared: 0.7161
F-statistic: 42.2 on 6 and 92 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6602 -0.4242 0.0010 0.4909 1.6757
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.284e+01 2.340e+00 -5.486 2.96e-07 ***
SWV 4.923e-03 3.376e-04 14.583 < 2e-16 ***
density 6.447e-03 1.890e-03 3.412 0.000924 ***
m_a2 6.458e-03 2.759e-03 2.340 0.021205 *
hw.vfrac -3.054e+00 1.275e+00 -2.394 0.018475 *
m_led 2.272e-03 1.172e-03 1.937 0.055445 .
m_whorliness -5.163e-01 2.709e-01 -1.906 0.059419 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7673 on 103 degrees of freedom
Multiple R-squared: 0.7478, Adjusted R-squared: 0.7331
F-statistic: 50.91 on 6 and 103 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_volume +
m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.4238 -0.3736 0.0428 0.4779 1.5865
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.188e+01 2.325e+00 -5.110 1.74e-06 ***
SWV 4.609e-03 3.504e-04 13.153 < 2e-16 ***
density 6.731e-03 1.890e-03 3.561 0.000587 ***
m_a2 7.243e-03 2.691e-03 2.691 0.008456 **
hw.vfrac -2.465e+00 1.297e+00 -1.901 0.060439 .
m_volume 8.792e-01 4.928e-01 1.784 0.077692 .
m_whorliness -4.505e-01 2.785e-01 -1.617 0.109204
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7585 on 92 degrees of freedom
Multiple R-squared: 0.7335, Adjusted R-squared: 0.7161
F-statistic: 42.2 on 6 and 92 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a0 + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.0939 -0.5006 -0.1318 0.6620 3.3537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.054e+01 2.430e+00 -4.337 3.33e-05 ***
SWV 3.440e-03 4.456e-04 7.720 7.14e-12 ***
density 1.073e-02 1.558e-03 6.883 4.40e-10 ***
m_a0 -5.882e-03 1.595e-03 -3.687 0.000361 ***
m_whorliness -1.020e+00 3.621e-01 -2.816 0.005802 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.063 on 105 degrees of freedom
Multiple R-squared: 0.5553, Adjusted R-squared: 0.5383
F-statistic: 32.77 on 4 and 105 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a0 + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-2.9370 -0.4571 -0.0470 0.5565 3.3829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.3682146 2.5937496 -3.612 0.000490 ***
SWV 0.0032613 0.0004737 6.885 6.43e-10 ***
density 0.0100358 0.0016349 6.138 1.97e-08 ***
m_a0 -0.0062816 0.0016654 -3.772 0.000283 ***
m_whorliness -0.8949541 0.3869892 -2.313 0.022926 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.078 on 94 degrees of freedom
Multiple R-squared: 0.5307, Adjusted R-squared: 0.5107
F-statistic: 26.57 on 4 and 94 DF, p-value: 9.378e-15
|
Call:
lm(formula = y ~ SWV + density + m_a1 + m_sweep1 + m_ovality +
hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.4250 -0.5269 -0.1105 0.5919 3.6464
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.370e+01 4.111e+00 -3.333 0.00126 **
SWV 6.240e-03 5.916e-04 10.547 < 2e-16 ***
density 5.777e-03 3.388e-03 1.705 0.09174 .
m_a1 -1.448e-02 4.471e-03 -3.238 0.00171 **
m_sweep1 -4.274e-01 2.128e-01 -2.009 0.04768 *
m_ovality 3.299e+02 1.791e+02 1.842 0.06886 .
hw.vfrac -3.839e+00 2.220e+00 -1.729 0.08728 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.248 on 87 degrees of freedom
Multiple R-squared: 0.656, Adjusted R-squared: 0.6322
F-statistic: 27.65 on 6 and 87 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a1 + m_sweep1 + m_ovality,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.9979 -0.6017 -0.0822 0.7391 2.8019
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.555e+01 3.333e+00 -4.666 1.27e-05 ***
SWV 5.580e-03 6.059e-04 9.209 4.76e-14 ***
density 9.160e-03 2.191e-03 4.182 7.59e-05 ***
m_a1 -1.308e-02 4.471e-03 -2.925 0.00453 **
m_sweep1 -5.275e-01 2.266e-01 -2.327 0.02257 *
m_ovality 3.247e+02 1.889e+02 1.718 0.08976 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.23 on 77 degrees of freedom
Multiple R-squared: 0.6308, Adjusted R-squared: 0.6068
F-statistic: 26.31 on 5 and 77 DF, p-value: 2.116e-15
|
Call:
lm(formula = y ~ SWV + m_a0 + m_sweep1 + m_ovality + m_a1 + m_whorliness +
m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.25566 -0.09036 -0.00736 0.06609 0.39768
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.308e+00 2.633e-01 4.966 3.43e-06 ***
SWV -1.163e-04 6.520e-05 -1.784 0.07796 .
m_a0 -6.415e-04 2.398e-04 -2.675 0.00895 **
m_sweep1 5.468e-02 2.293e-02 2.385 0.01929 *
m_ovality -4.362e+01 1.936e+01 -2.253 0.02678 *
m_a1 2.888e-03 1.188e-03 2.431 0.01715 *
m_whorliness -1.182e-01 5.225e-02 -2.263 0.02617 *
m_waist 1.531e-01 1.053e-01 1.454 0.14958
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1338 on 86 degrees of freedom
Multiple R-squared: 0.2895, Adjusted R-squared: 0.2317
F-statistic: 5.007 on 7 and 86 DF, p-value: 8.649e-05
|
Call:
lm(formula = y ~ SWV + m_a0 + m_sweep1 + m_ovality + m_a1 + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.25844 -0.07241 -0.02548 0.05884 0.42564
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.357e+00 2.758e-01 4.918 4.93e-06 ***
SWV -1.269e-04 6.898e-05 -1.840 0.0696 .
m_a0 -5.933e-04 2.513e-04 -2.361 0.0208 *
m_sweep1 6.281e-02 2.618e-02 2.399 0.0189 *
m_ovality -4.587e+01 2.163e+01 -2.120 0.0372 *
m_a1 1.182e-03 5.419e-04 2.182 0.0322 *
m_whorliness -7.906e-02 5.455e-02 -1.449 0.1514
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1395 on 76 degrees of freedom
Multiple R-squared: 0.274, Adjusted R-squared: 0.2167
F-statistic: 4.781 on 6 and 76 DF, p-value: 0.0003479
|
Call:
lm(formula = y ~ density + sweep.prod + m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.3170 -1.4776 -0.2643 1.0128 10.4716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.653904 2.953953 3.945 0.000144 ***
density -0.007949 0.003093 -2.570 0.011562 *
sweep.prod 2.111823 0.877651 2.406 0.017849 *
m_waist -1.155524 0.645883 -1.789 0.076461 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.198 on 106 degrees of freedom
Multiple R-squared: 0.1473, Adjusted R-squared: 0.1232
F-statistic: 6.105 on 3 and 106 DF, p-value: 0.0007164
|
Call:
lm(formula = y ~ SWV + density + sweep.prod, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.3097 -1.1490 -0.2803 0.9707 7.0408
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.824808 4.273469 4.639 1.12e-05 ***
SWV -0.001852 0.000787 -2.353 0.020675 *
density -0.010220 0.002713 -3.767 0.000287 ***
sweep.prod 1.342889 0.775881 1.731 0.086735 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.769 on 95 degrees of freedom
Multiple R-squared: 0.2106, Adjusted R-squared: 0.1857
F-statistic: 8.448 on 3 and 95 DF, p-value: 4.94e-05
|
Call:
lm(formula = y ~ density + m_sed + m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-6.288 -2.223 -0.860 1.490 13.596
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.008413 4.365237 3.209 0.00176 **
density -0.013907 0.004708 -2.954 0.00386 **
m_sed 0.015127 0.005089 2.973 0.00366 **
m_waist -2.070118 1.013245 -2.043 0.04353 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.328 on 106 degrees of freedom
Multiple R-squared: 0.1365, Adjusted R-squared: 0.112
F-statistic: 5.585 on 3 and 106 DF, p-value: 0.001352
|
Call:
lm(formula = y ~ SWV + density + m_volume + m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.4912 -2.1006 -0.5942 1.6468 9.6802
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.964349 6.626695 3.616 0.000483 ***
SWV -0.002201 0.001284 -1.714 0.089836 .
density -0.014478 0.004331 -3.343 0.001192 **
m_volume 5.420538 1.842224 2.942 0.004102 **
m_waist -1.361540 0.911003 -1.495 0.138381
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.885 on 94 degrees of freedom
Multiple R-squared: 0.1912, Adjusted R-squared: 0.1568
F-statistic: 5.556 on 4 and 94 DF, p-value: 0.0004653
|
Call:
lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.452 -1.613 -0.563 1.139 10.283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.452262 3.664267 3.398 0.0010 **
SWV -0.002389 0.001109 -2.154 0.0339 *
m_waist -49.942189 32.476941 -1.538 0.1275
m_a2 0.530931 0.354792 1.496 0.1380
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.536 on 92 degrees of freedom
Multiple R-squared: 0.1016, Adjusted R-squared: 0.07232
F-statistic: 3.469 on 3 and 92 DF, p-value: 0.01934
|
Call:
lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.2440 -1.4690 -0.3849 1.1939 10.0172
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.129999 3.438042 4.110 9.42e-05 ***
SWV -0.002976 0.001043 -2.854 0.00548 **
m_waist -53.362893 30.807196 -1.732 0.08705 .
m_a2 0.570384 0.336274 1.696 0.09369 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.275 on 81 degrees of freedom
Multiple R-squared: 0.1489, Adjusted R-squared: 0.1174
F-statistic: 4.725 on 3 and 81 DF, p-value: 0.004339
|
Call:
lm(formula = y ~ SWV + density + m_taper + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.3691 -2.2349 -0.4538 1.6654 16.0980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.501185 7.972952 6.083 1.94e-08 ***
SWV -0.006148 0.001506 -4.083 8.70e-05 ***
density -0.014984 0.005031 -2.978 0.0036 **
m_taper -0.222586 0.131070 -1.698 0.0924 .
m_whorliness -2.041574 1.258311 -1.622 0.1077
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.451 on 105 degrees of freedom
Multiple R-squared: 0.189, Adjusted R-squared: 0.1581
F-statistic: 6.119 on 4 and 105 DF, p-value: 0.0001824
|
Call:
lm(formula = y ~ SWV + m_taper + density, data = LL)
Residuals:
Min 1Q Median 3Q Max
-7.8825 -2.1014 -0.2936 2.0101 8.9927
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.332042 7.417488 5.842 7.21e-08 ***
SWV -0.005835 0.001421 -4.105 8.54e-05 ***
m_taper -0.350933 0.115328 -3.043 0.00303 **
density -0.011328 0.004671 -2.425 0.01719 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.144 on 95 degrees of freedom
Multiple R-squared: 0.1957, Adjusted R-squared: 0.1703
F-statistic: 7.703 on 3 and 95 DF, p-value: 0.0001163
|
Call:
lm(formula = y ~ SWV + m_ovality + density + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.8071 -3.4889 -0.9923 2.6980 17.9881
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.163e+01 1.133e+01 4.559 1.4e-05 ***
SWV -7.409e-03 2.182e-03 -3.395 0.00097 ***
m_ovality 1.994e+03 7.068e+02 2.821 0.00573 **
density -1.666e-02 7.329e-03 -2.273 0.02507 *
m_a1 -3.115e-02 1.721e-02 -1.810 0.07311 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.11 on 105 degrees of freedom
Multiple R-squared: 0.1666, Adjusted R-squared: 0.1349
F-statistic: 5.249 on 4 and 105 DF, p-value: 0.0006807
|
Call:
lm(formula = y ~ SWV + m_taper + density + m_ovality + m_a2,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.4699 -3.4732 -0.7993 2.6272 17.1912
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.435e+01 1.183e+01 4.594 1.36e-05 ***
SWV -8.285e-03 2.273e-03 -3.645 0.00044 ***
m_taper -4.257e-01 1.870e-01 -2.276 0.02513 *
density -1.343e-02 7.275e-03 -1.846 0.06814 .
m_ovality 1.196e+03 7.226e+02 1.656 0.10117
m_a2 2.488e-02 1.685e-02 1.477 0.14314
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.876 on 93 degrees of freedom
Multiple R-squared: 0.1574, Adjusted R-squared: 0.1121
F-statistic: 3.475 on 5 and 93 DF, p-value: 0.006346
|
Call:
lm(formula = y ~ SWV + density + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.8770 -2.9641 -0.8829 2.2955 29.8845
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.793153 14.951333 5.136 1.56e-06 ***
SWV -0.011094 0.002821 -3.933 0.000163 ***
density -0.027576 0.009940 -2.774 0.006698 **
m_taper -0.484845 0.215945 -2.245 0.027149 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.003 on 92 degrees of freedom
Multiple R-squared: 0.1883, Adjusted R-squared: 0.1618
F-statistic: 7.112 on 3 and 92 DF, p-value: 0.000238
|
Call:
lm(formula = y ~ SWV + density + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.688 -3.494 -1.154 2.504 30.197
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.851653 16.191384 4.561 1.79e-05 ***
SWV -0.010823 0.003025 -3.578 0.000589 ***
density -0.025032 0.010657 -2.349 0.021272 *
m_taper -0.526517 0.231231 -2.277 0.025423 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.156 on 81 degrees of freedom
Multiple R-squared: 0.1778, Adjusted R-squared: 0.1474
F-statistic: 5.84 on 3 and 81 DF, p-value: 0.001157
|
Call:
lm(formula = y ~ SWV + m_led + density, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.248 -1.267 0.010 1.006 4.360
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.7109424 3.6064887 3.802 0.00024 ***
SWV -0.0038241 0.0006847 -5.585 1.82e-07 ***
m_led -0.0139476 0.0022681 -6.150 1.40e-08 ***
density 0.0073719 0.0023770 3.101 0.00247 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.64 on 106 degrees of freedom
Multiple R-squared: 0.3886, Adjusted R-squared: 0.3713
F-statistic: 22.46 on 3 and 106 DF, p-value: 2.469e-11
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.4235 -1.3939 0.0823 1.0316 4.1310
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.4494854 3.8816697 3.722 0.000336 ***
SWV -0.0038770 0.0007339 -5.282 8.21e-07 ***
m_led -0.0300531 0.0105285 -2.854 0.005304 **
density 0.0072377 0.0024971 2.898 0.004666 **
m_sed 0.0172826 0.0113627 1.521 0.131617
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.666 on 94 degrees of freedom
Multiple R-squared: 0.4124, Adjusted R-squared: 0.3874
F-statistic: 16.49 on 4 and 94 DF, p-value: 2.865e-10
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed + m_sweep1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.582 -1.796 -0.130 1.532 5.547
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.7874359 5.2124739 2.453 0.015820 *
SWV -0.0039318 0.0009836 -3.997 0.000120 ***
m_led -0.0519144 0.0141102 -3.679 0.000373 ***
density 0.0138862 0.0033889 4.098 8.29e-05 ***
m_sed 0.0363740 0.0152884 2.379 0.019175 *
m_sweep1 -0.5420807 0.3583244 -1.513 0.133358
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.327 on 104 degrees of freedom
Multiple R-squared: 0.393, Adjusted R-squared: 0.3638
F-statistic: 13.47 on 5 and 104 DF, p-value: 3.999e-10
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.4742 -2.0759 -0.1849 1.6187 5.1347
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.602588 5.419818 1.772 0.079676 .
SWV -0.003382 0.001025 -3.300 0.001365 **
m_led -0.057651 0.014701 -3.922 0.000167 ***
density 0.015139 0.003487 4.342 3.56e-05 ***
m_sed 0.041572 0.015865 2.620 0.010243 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.327 on 94 degrees of freedom
Multiple R-squared: 0.4061, Adjusted R-squared: 0.3809
F-statistic: 16.07 on 4 and 94 DF, p-value: 4.636e-10
|
2.4.2 JH ‘well-represented’ logs
- chosen by vfrac rec
rec = L$nboards.complete/L$volume
for (lmeas in log.quality.measures) {
models <- cf.full(lmeas, idx.sub=rec>quantile(rec, 0.8))
}
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6602 -0.4242 0.0010 0.4909 1.6757
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.284e+01 2.340e+00 -5.486 2.96e-07 ***
SWV 4.923e-03 3.376e-04 14.583 < 2e-16 ***
density 6.447e-03 1.890e-03 3.412 0.000924 ***
m_a2 6.458e-03 2.759e-03 2.340 0.021205 *
hw.vfrac -3.054e+00 1.275e+00 -2.394 0.018475 *
m_led 2.272e-03 1.172e-03 1.937 0.055445 .
m_whorliness -5.163e-01 2.709e-01 -1.906 0.059419 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7673 on 103 degrees of freedom
Multiple R-squared: 0.7478, Adjusted R-squared: 0.7331
F-statistic: 50.91 on 6 and 103 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_led + m_a1 + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.88746 -0.25900 -0.03626 0.24653 0.69369
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.267e+01 2.648e+00 -8.564 9.16e-08 ***
SWV 7.154e-03 5.012e-04 14.274 2.95e-11 ***
density 6.552e-03 1.828e-03 3.584 0.00212 **
m_led 7.109e-03 1.827e-03 3.891 0.00107 **
m_a1 -1.291e-02 4.241e-03 -3.045 0.00697 **
m_whorliness 6.390e-01 4.722e-01 1.353 0.19279
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4357 on 18 degrees of freedom
Multiple R-squared: 0.9421, Adjusted R-squared: 0.926
F-statistic: 58.52 on 5 and 18 DF, p-value: 1.678e-10
|
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6602 -0.4242 0.0010 0.4909 1.6757
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.284e+01 2.340e+00 -5.486 2.96e-07 ***
SWV 4.923e-03 3.376e-04 14.583 < 2e-16 ***
density 6.447e-03 1.890e-03 3.412 0.000924 ***
m_a2 6.458e-03 2.759e-03 2.340 0.021205 *
hw.vfrac -3.054e+00 1.275e+00 -2.394 0.018475 *
m_led 2.272e-03 1.172e-03 1.937 0.055445 .
m_whorliness -5.163e-01 2.709e-01 -1.906 0.059419 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7673 on 103 degrees of freedom
Multiple R-squared: 0.7478, Adjusted R-squared: 0.7331
F-statistic: 50.91 on 6 and 103 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_led + m_a1 + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.88746 -0.25900 -0.03626 0.24653 0.69369
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.267e+01 2.648e+00 -8.564 9.16e-08 ***
SWV 7.154e-03 5.012e-04 14.274 2.95e-11 ***
density 6.552e-03 1.828e-03 3.584 0.00212 **
m_led 7.109e-03 1.827e-03 3.891 0.00107 **
m_a1 -1.291e-02 4.241e-03 -3.045 0.00697 **
m_whorliness 6.390e-01 4.722e-01 1.353 0.19279
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4357 on 18 degrees of freedom
Multiple R-squared: 0.9421, Adjusted R-squared: 0.926
F-statistic: 58.52 on 5 and 18 DF, p-value: 1.678e-10
|
Call:
lm(formula = y ~ SWV + density + m_a0 + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.0939 -0.5006 -0.1318 0.6620 3.3537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.054e+01 2.430e+00 -4.337 3.33e-05 ***
SWV 3.440e-03 4.456e-04 7.720 7.14e-12 ***
density 1.073e-02 1.558e-03 6.883 4.40e-10 ***
m_a0 -5.882e-03 1.595e-03 -3.687 0.000361 ***
m_whorliness -1.020e+00 3.621e-01 -2.816 0.005802 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.063 on 105 degrees of freedom
Multiple R-squared: 0.5553, Adjusted R-squared: 0.5383
F-statistic: 32.77 on 4 and 105 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + hw.vfrac + m_a0 + m_sweep2 + m_sed + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.94428 -0.33315 0.00002 0.27278 1.08558
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.159e-01 3.183e+00 0.193 0.8489
SWV 3.683e-03 6.905e-04 5.333 5.49e-05 ***
hw.vfrac -1.029e+01 1.922e+00 -5.352 5.28e-05 ***
m_a0 -6.450e-02 2.630e-02 -2.452 0.0253 *
m_sweep2 -3.495e+00 1.383e+00 -2.527 0.0217 *
m_sed 5.825e-02 2.621e-02 2.223 0.0401 *
m_whorliness 1.127e+00 6.996e-01 1.610 0.1257
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5922 on 17 degrees of freedom
Multiple R-squared: 0.8664, Adjusted R-squared: 0.8193
F-statistic: 18.38 on 6 and 17 DF, p-value: 1.431e-06
|
Call:
lm(formula = y ~ SWV + density + m_a1 + m_sweep1 + m_ovality +
hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.4250 -0.5269 -0.1105 0.5919 3.6464
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.370e+01 4.111e+00 -3.333 0.00126 **
SWV 6.240e-03 5.916e-04 10.547 < 2e-16 ***
density 5.777e-03 3.388e-03 1.705 0.09174 .
m_a1 -1.448e-02 4.471e-03 -3.238 0.00171 **
m_sweep1 -4.274e-01 2.128e-01 -2.009 0.04768 *
m_ovality 3.299e+02 1.791e+02 1.842 0.06886 .
hw.vfrac -3.839e+00 2.220e+00 -1.729 0.08728 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.248 on 87 degrees of freedom
Multiple R-squared: 0.656, Adjusted R-squared: 0.6322
F-statistic: 27.65 on 6 and 87 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + m_a2 + m_a0 + density + weight, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.3501 -0.4890 -0.1390 0.4781 1.7667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -37.430072 5.529941 -6.769 3.28e-06 ***
SWV 0.009902 0.000872 11.356 2.33e-09 ***
m_a2 0.024085 0.009928 2.426 0.0267 *
m_a0 0.034455 0.018278 1.885 0.0766 .
density 0.008899 0.003405 2.614 0.0181 *
weight -0.008844 0.006440 -1.373 0.1875
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7873 on 17 degrees of freedom
Multiple R-squared: 0.9122, Adjusted R-squared: 0.8863
F-statistic: 35.31 on 5 and 17 DF, p-value: 2.123e-08
|
Call:
lm(formula = y ~ SWV + m_a0 + m_sweep1 + m_ovality + m_a1 + m_whorliness +
m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.25566 -0.09036 -0.00736 0.06609 0.39768
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.308e+00 2.633e-01 4.966 3.43e-06 ***
SWV -1.163e-04 6.520e-05 -1.784 0.07796 .
m_a0 -6.415e-04 2.398e-04 -2.675 0.00895 **
m_sweep1 5.468e-02 2.293e-02 2.385 0.01929 *
m_ovality -4.362e+01 1.936e+01 -2.253 0.02678 *
m_a1 2.888e-03 1.188e-03 2.431 0.01715 *
m_whorliness -1.182e-01 5.225e-02 -2.263 0.02617 *
m_waist 1.531e-01 1.053e-01 1.454 0.14958
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1338 on 86 degrees of freedom
Multiple R-squared: 0.2895, Adjusted R-squared: 0.2317
F-statistic: 5.007 on 7 and 86 DF, p-value: 8.649e-05
|
Call:
lm(formula = y ~ SWV + m_a0 + m_volume + m_led + hw.vfrac + m_sed,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.11060 -0.04572 -0.00163 0.02654 0.12192
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.142e+00 4.717e-01 8.780 1.63e-07 ***
SWV -3.785e-04 8.198e-05 -4.617 0.000285 ***
m_a0 -1.153e-02 3.594e-03 -3.208 0.005484 **
m_volume 3.470e+00 7.989e-01 4.344 0.000502 ***
m_led -3.511e-03 1.857e-03 -1.891 0.076924 .
hw.vfrac -6.370e-01 2.355e-01 -2.705 0.015616 *
m_sed 4.684e-03 3.494e-03 1.340 0.198842
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07049 on 16 degrees of freedom
Multiple R-squared: 0.7997, Adjusted R-squared: 0.7246
F-statistic: 10.65 on 6 and 16 DF, p-value: 7.877e-05
|
Call:
lm(formula = y ~ density + sweep.prod + m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.3170 -1.4776 -0.2643 1.0128 10.4716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.653904 2.953953 3.945 0.000144 ***
density -0.007949 0.003093 -2.570 0.011562 *
sweep.prod 2.111823 0.877651 2.406 0.017849 *
m_waist -1.155524 0.645883 -1.789 0.076461 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.198 on 106 degrees of freedom
Multiple R-squared: 0.1473, Adjusted R-squared: 0.1232
F-statistic: 6.105 on 3 and 106 DF, p-value: 0.0007164
|
Call:
lm(formula = y ~ 1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-2.7775 -1.8803 -0.3013 0.7761 11.8475
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.7775 0.6014 7.944 4.84e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.946 on 23 degrees of freedom
|
Call:
lm(formula = y ~ density + m_sed + m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-6.288 -2.223 -0.860 1.490 13.596
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.008413 4.365237 3.209 0.00176 **
density -0.013907 0.004708 -2.954 0.00386 **
m_sed 0.015127 0.005089 2.973 0.00366 **
m_waist -2.070118 1.013245 -2.043 0.04353 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.328 on 106 degrees of freedom
Multiple R-squared: 0.1365, Adjusted R-squared: 0.112
F-statistic: 5.585 on 3 and 106 DF, p-value: 0.001352
|
Call:
lm(formula = y ~ m_sed + weight, data = LL)
Residuals:
Min 1Q Median 3Q Max
-7.4673 -1.9309 -0.2402 1.5298 11.1283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -35.35222 11.92359 -2.965 0.00739 **
m_sed 0.22457 0.07009 3.204 0.00426 **
weight -0.06864 0.02390 -2.872 0.00912 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.545 on 21 degrees of freedom
Multiple R-squared: 0.4005, Adjusted R-squared: 0.3434
F-statistic: 7.014 on 2 and 21 DF, p-value: 0.004644
|
Call:
lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.452 -1.613 -0.563 1.139 10.283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.452262 3.664267 3.398 0.0010 **
SWV -0.002389 0.001109 -2.154 0.0339 *
m_waist -49.942189 32.476941 -1.538 0.1275
m_a2 0.530931 0.354792 1.496 0.1380
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.536 on 92 degrees of freedom
Multiple R-squared: 0.1016, Adjusted R-squared: 0.07232
F-statistic: 3.469 on 3 and 92 DF, p-value: 0.01934
|
Call:
lm(formula = y ~ 1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-2.4812 -1.4812 -0.6812 1.0605 5.5188
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.4812 0.4425 10.13 9.59e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.122 on 22 degrees of freedom
|
Call:
lm(formula = y ~ SWV + density + m_taper + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.3691 -2.2349 -0.4538 1.6654 16.0980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.501185 7.972952 6.083 1.94e-08 ***
SWV -0.006148 0.001506 -4.083 8.70e-05 ***
density -0.014984 0.005031 -2.978 0.0036 **
m_taper -0.222586 0.131070 -1.698 0.0924 .
m_whorliness -2.041574 1.258311 -1.622 0.1077
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.451 on 105 degrees of freedom
Multiple R-squared: 0.189, Adjusted R-squared: 0.1581
F-statistic: 6.119 on 4 and 105 DF, p-value: 0.0001824
|
Call:
lm(formula = y ~ SWV + weight, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.9310 -1.3120 -0.0798 1.3159 6.4601
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.719501 11.049087 4.500 0.000197 ***
SWV -0.010910 0.003041 -3.588 0.001734 **
weight -0.007597 0.003765 -2.018 0.056573 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.837 on 21 degrees of freedom
Multiple R-squared: 0.3822, Adjusted R-squared: 0.3233
F-statistic: 6.495 on 2 and 21 DF, p-value: 0.006368
|
Call:
lm(formula = y ~ SWV + m_ovality + density + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.8071 -3.4889 -0.9923 2.6980 17.9881
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.163e+01 1.133e+01 4.559 1.4e-05 ***
SWV -7.409e-03 2.182e-03 -3.395 0.00097 ***
m_ovality 1.994e+03 7.068e+02 2.821 0.00573 **
density -1.666e-02 7.329e-03 -2.273 0.02507 *
m_a1 -3.115e-02 1.721e-02 -1.810 0.07311 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.11 on 105 degrees of freedom
Multiple R-squared: 0.1666, Adjusted R-squared: 0.1349
F-statistic: 5.249 on 4 and 105 DF, p-value: 0.0006807
|
Call:
lm(formula = y ~ SWV + weight + m_a0, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.1079 -3.0579 -0.0426 2.2918 9.2032
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 38.562970 22.999889 1.677 0.1092
SWV -0.013006 0.004798 -2.711 0.0135 *
weight -0.051554 0.030385 -1.697 0.1053
m_a0 0.120156 0.087093 1.380 0.1829
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.473 on 20 degrees of freedom
Multiple R-squared: 0.3226, Adjusted R-squared: 0.221
F-statistic: 3.175 on 3 and 20 DF, p-value: 0.04655
|
Call:
lm(formula = y ~ SWV + density + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.8770 -2.9641 -0.8829 2.2955 29.8845
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.793153 14.951333 5.136 1.56e-06 ***
SWV -0.011094 0.002821 -3.933 0.000163 ***
density -0.027576 0.009940 -2.774 0.006698 **
m_taper -0.484845 0.215945 -2.245 0.027149 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.003 on 92 degrees of freedom
Multiple R-squared: 0.1883, Adjusted R-squared: 0.1618
F-statistic: 7.112 on 3 and 92 DF, p-value: 0.000238
|
Call:
lm(formula = y ~ SWV + sweep.prod + m_whorliness + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-6.3792 -1.5763 -0.4494 2.0104 6.1725
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.759344 10.467009 4.754 0.000159 ***
SWV -0.010177 0.002805 -3.627 0.001926 **
sweep.prod 6.748296 2.356017 2.864 0.010310 *
m_whorliness -6.854258 3.278325 -2.091 0.051003 .
m_taper -0.558161 0.272914 -2.045 0.055738 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.038 on 18 degrees of freedom
Multiple R-squared: 0.5618, Adjusted R-squared: 0.4644
F-statistic: 5.77 on 4 and 18 DF, p-value: 0.003607
|
Call:
lm(formula = y ~ SWV + m_led + density, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.248 -1.267 0.010 1.006 4.360
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.7109424 3.6064887 3.802 0.00024 ***
SWV -0.0038241 0.0006847 -5.585 1.82e-07 ***
m_led -0.0139476 0.0022681 -6.150 1.40e-08 ***
density 0.0073719 0.0023770 3.101 0.00247 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.64 on 106 degrees of freedom
Multiple R-squared: 0.3886, Adjusted R-squared: 0.3713
F-statistic: 22.46 on 3 and 106 DF, p-value: 2.469e-11
|
Call:
lm(formula = y ~ SWV + m_volume + weight, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.2224 -0.7616 0.2203 1.0104 2.4579
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.471236 5.865099 3.320 0.00342 **
SWV -0.004294 0.001600 -2.684 0.01427 *
m_volume -27.130566 14.671475 -1.849 0.07926 .
weight 0.021518 0.013379 1.608 0.12344
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.487 on 20 degrees of freedom
Multiple R-squared: 0.3305, Adjusted R-squared: 0.2301
F-statistic: 3.291 on 3 and 20 DF, p-value: 0.04178
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed + m_sweep1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.582 -1.796 -0.130 1.532 5.547
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.7874359 5.2124739 2.453 0.015820 *
SWV -0.0039318 0.0009836 -3.997 0.000120 ***
m_led -0.0519144 0.0141102 -3.679 0.000373 ***
density 0.0138862 0.0033889 4.098 8.29e-05 ***
m_sed 0.0363740 0.0152884 2.379 0.019175 *
m_sweep1 -0.5420807 0.3583244 -1.513 0.133358
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.327 on 104 degrees of freedom
Multiple R-squared: 0.393, Adjusted R-squared: 0.3638
F-statistic: 13.47 on 5 and 104 DF, p-value: 3.999e-10
|
Call:
lm(formula = y ~ SWV + m_volume + weight + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.8823 -1.1936 -0.3458 1.1601 5.8986
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.963402 11.357559 3.343 0.00342 **
SWV -0.007732 0.002845 -2.718 0.01366 *
m_volume -54.638209 24.759262 -2.207 0.03983 *
weight 0.042520 0.022535 1.887 0.07456 .
m_whorliness -3.987669 2.712342 -1.470 0.15788
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.504 on 19 degrees of freedom
Multiple R-squared: 0.3768, Adjusted R-squared: 0.2456
F-statistic: 2.872 on 4 and 19 DF, p-value: 0.05124
|
- Chosen by visual inspection of saw patterns
xyplot(rec ~ volume, L, group=SWILogNumber%in%near.complete.sawpatterns)
for (lmeas in log.quality.measures) {
models <- cf.full(lmeas, idx.sub=L$SWILogNumber%in%near.complete.sawpatterns)
}
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6602 -0.4242 0.0010 0.4909 1.6757
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.284e+01 2.340e+00 -5.486 2.96e-07 ***
SWV 4.923e-03 3.376e-04 14.583 < 2e-16 ***
density 6.447e-03 1.890e-03 3.412 0.000924 ***
m_a2 6.458e-03 2.759e-03 2.340 0.021205 *
hw.vfrac -3.054e+00 1.275e+00 -2.394 0.018475 *
m_led 2.272e-03 1.172e-03 1.937 0.055445 .
m_whorliness -5.163e-01 2.709e-01 -1.906 0.059419 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7673 on 103 degrees of freedom
Multiple R-squared: 0.7478, Adjusted R-squared: 0.7331
F-statistic: 50.91 on 6 and 103 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + weight + m_a0, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.70670 -0.18717 0.09253 0.29746 0.49427
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.005e+01 3.183e+00 -6.299 1.06e-05 ***
SWV 6.661e-03 4.793e-04 13.900 2.38e-10 ***
density 9.728e-03 2.164e-03 4.495 0.000367 ***
weight 6.061e-03 2.762e-03 2.195 0.043275 *
m_a0 -1.323e-02 7.705e-03 -1.717 0.105287
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3604 on 16 degrees of freedom
Multiple R-squared: 0.9514, Adjusted R-squared: 0.9393
F-statistic: 78.34 on 4 and 16 DF, p-value: 2.67e-10
|
Call:
lm(formula = y ~ SWV + density + m_a2 + hw.vfrac + m_led + m_whorliness,
data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.6602 -0.4242 0.0010 0.4909 1.6757
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.284e+01 2.340e+00 -5.486 2.96e-07 ***
SWV 4.923e-03 3.376e-04 14.583 < 2e-16 ***
density 6.447e-03 1.890e-03 3.412 0.000924 ***
m_a2 6.458e-03 2.759e-03 2.340 0.021205 *
hw.vfrac -3.054e+00 1.275e+00 -2.394 0.018475 *
m_led 2.272e-03 1.172e-03 1.937 0.055445 .
m_whorliness -5.163e-01 2.709e-01 -1.906 0.059419 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7673 on 103 degrees of freedom
Multiple R-squared: 0.7478, Adjusted R-squared: 0.7331
F-statistic: 50.91 on 6 and 103 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + weight + m_a0, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.70670 -0.18717 0.09253 0.29746 0.49427
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.005e+01 3.183e+00 -6.299 1.06e-05 ***
SWV 6.661e-03 4.793e-04 13.900 2.38e-10 ***
density 9.728e-03 2.164e-03 4.495 0.000367 ***
weight 6.061e-03 2.762e-03 2.195 0.043275 *
m_a0 -1.323e-02 7.705e-03 -1.717 0.105287
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3604 on 16 degrees of freedom
Multiple R-squared: 0.9514, Adjusted R-squared: 0.9393
F-statistic: 78.34 on 4 and 16 DF, p-value: 2.67e-10
|
Call:
lm(formula = y ~ SWV + density + m_a0 + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.0939 -0.5006 -0.1318 0.6620 3.3537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.054e+01 2.430e+00 -4.337 3.33e-05 ***
SWV 3.440e-03 4.456e-04 7.720 7.14e-12 ***
density 1.073e-02 1.558e-03 6.883 4.40e-10 ***
m_a0 -5.882e-03 1.595e-03 -3.687 0.000361 ***
m_whorliness -1.020e+00 3.621e-01 -2.816 0.005802 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.063 on 105 degrees of freedom
Multiple R-squared: 0.5553, Adjusted R-squared: 0.5383
F-statistic: 32.77 on 4 and 105 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_a0, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.1435 -0.5177 -0.2974 0.2317 3.1009
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -13.221614 6.788385 -1.948 0.06816 .
SWV 0.004603 0.001351 3.407 0.00336 **
density 0.009881 0.005123 1.929 0.07062 .
m_a0 -0.008189 0.004271 -1.917 0.07214 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.024 on 17 degrees of freedom
Multiple R-squared: 0.7286, Adjusted R-squared: 0.6807
F-statistic: 15.22 on 3 and 17 DF, p-value: 4.574e-05
|
Call:
lm(formula = y ~ SWV + density + m_a1 + m_sweep1 + m_ovality +
hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.4250 -0.5269 -0.1105 0.5919 3.6464
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.370e+01 4.111e+00 -3.333 0.00126 **
SWV 6.240e-03 5.916e-04 10.547 < 2e-16 ***
density 5.777e-03 3.388e-03 1.705 0.09174 .
m_a1 -1.448e-02 4.471e-03 -3.238 0.00171 **
m_sweep1 -4.274e-01 2.128e-01 -2.009 0.04768 *
m_ovality 3.299e+02 1.791e+02 1.842 0.06886 .
hw.vfrac -3.839e+00 2.220e+00 -1.729 0.08728 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.248 on 87 degrees of freedom
Multiple R-squared: 0.656, Adjusted R-squared: 0.6322
F-statistic: 27.65 on 6 and 87 DF, p-value: < 2.2e-16
|
Call:
lm(formula = y ~ SWV + density + m_ovality + m_taper + m_volume +
m_whorliness + m_sweep1 + hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.29778 -0.18770 -0.08215 0.20673 0.51244
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.689e+01 2.516e+00 -10.688 8.61e-07 ***
SWV 7.296e-03 5.162e-04 14.135 6.18e-08 ***
density 1.370e-02 1.768e-03 7.748 1.56e-05 ***
m_ovality 3.858e+02 8.996e+01 4.289 0.00159 **
m_taper -8.456e-02 2.654e-02 -3.187 0.00971 **
m_volume 1.685e+00 6.502e-01 2.592 0.02686 *
m_whorliness 1.099e+00 3.989e-01 2.756 0.02028 *
m_sweep1 -2.341e-01 1.422e-01 -1.646 0.13079
hw.vfrac -1.814e+00 1.660e+00 -1.092 0.30025
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3199 on 10 degrees of freedom
Multiple R-squared: 0.9788, Adjusted R-squared: 0.9619
F-statistic: 57.78 on 8 and 10 DF, p-value: 2.26e-07
|
Call:
lm(formula = y ~ SWV + m_a0 + m_sweep1 + m_ovality + m_a1 + m_whorliness +
m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.25566 -0.09036 -0.00736 0.06609 0.39768
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.308e+00 2.633e-01 4.966 3.43e-06 ***
SWV -1.163e-04 6.520e-05 -1.784 0.07796 .
m_a0 -6.415e-04 2.398e-04 -2.675 0.00895 **
m_sweep1 5.468e-02 2.293e-02 2.385 0.01929 *
m_ovality -4.362e+01 1.936e+01 -2.253 0.02678 *
m_a1 2.888e-03 1.188e-03 2.431 0.01715 *
m_whorliness -1.182e-01 5.225e-02 -2.263 0.02617 *
m_waist 1.531e-01 1.053e-01 1.454 0.14958
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1338 on 86 degrees of freedom
Multiple R-squared: 0.2895, Adjusted R-squared: 0.2317
F-statistic: 5.007 on 7 and 86 DF, p-value: 8.649e-05
|
Call:
lm(formula = y ~ m_sed + m_led + m_ovality + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-0.14159 -0.06416 -0.02260 0.01966 0.39860
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.206767 0.184817 6.530 1.34e-05 ***
m_sed -0.005454 0.001974 -2.763 0.0153 *
m_led 0.004393 0.001791 2.453 0.0279 *
m_ovality -61.808328 35.285081 -1.752 0.1017
m_whorliness -0.235371 0.140462 -1.676 0.1160
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.132 on 14 degrees of freedom
Multiple R-squared: 0.4577, Adjusted R-squared: 0.3028
F-statistic: 2.954 on 4 and 14 DF, p-value: 0.05797
|
Call:
lm(formula = y ~ density + sweep.prod + m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.3170 -1.4776 -0.2643 1.0128 10.4716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.653904 2.953953 3.945 0.000144 ***
density -0.007949 0.003093 -2.570 0.011562 *
sweep.prod 2.111823 0.877651 2.406 0.017849 *
m_waist -1.155524 0.645883 -1.789 0.076461 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.198 on 106 degrees of freedom
Multiple R-squared: 0.1473, Adjusted R-squared: 0.1232
F-statistic: 6.105 on 3 and 106 DF, p-value: 0.0007164
|
Call:
lm(formula = y ~ 1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-2.8139 -1.3834 -0.9012 0.4361 11.3666
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.258 0.649 8.102 9.58e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.974 on 20 degrees of freedom
|
Call:
lm(formula = y ~ density + m_sed + m_waist, data = LL)
Residuals:
Min 1Q Median 3Q Max
-6.288 -2.223 -0.860 1.490 13.596
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.008413 4.365237 3.209 0.00176 **
density -0.013907 0.004708 -2.954 0.00386 **
m_sed 0.015127 0.005089 2.973 0.00366 **
m_waist -2.070118 1.013245 -2.043 0.04353 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.328 on 106 degrees of freedom
Multiple R-squared: 0.1365, Adjusted R-squared: 0.112
F-statistic: 5.585 on 3 and 106 DF, p-value: 0.001352
|
Call:
lm(formula = y ~ 1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.6944 -2.4611 -0.2111 1.0389 13.3056
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.2944 0.8281 7.601 2.55e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.795 on 20 degrees of freedom
|
Call:
lm(formula = y ~ SWV + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.452 -1.613 -0.563 1.139 10.283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.452262 3.664267 3.398 0.0010 **
SWV -0.002389 0.001109 -2.154 0.0339 *
m_waist -49.942189 32.476941 -1.538 0.1275
m_a2 0.530931 0.354792 1.496 0.1380
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.536 on 92 degrees of freedom
Multiple R-squared: 0.1016, Adjusted R-squared: 0.07232
F-statistic: 3.469 on 3 and 92 DF, p-value: 0.01934
|
Call:
lm(formula = y ~ SWV + m_a0 + m_volume + m_sed + weight + m_a1 +
m_led + m_taper + m_waist + m_a2, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.7645 -0.5759 0.2205 0.7094 1.3483
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.517e+01 1.926e+01 4.423 0.00166 **
SWV -1.389e-02 3.555e-03 -3.906 0.00359 **
m_a0 -1.530e-01 1.268e-01 -1.207 0.25837
m_volume 1.219e+02 3.323e+01 3.669 0.00516 **
m_sed 2.630e-01 7.630e-02 3.447 0.00731 **
weight -7.660e-02 2.161e-02 -3.545 0.00626 **
m_a1 -1.666e+00 7.629e-01 -2.183 0.05688 .
m_led -2.719e-01 7.152e-02 -3.802 0.00420 **
m_taper 8.904e+00 3.706e+00 2.403 0.03972 *
m_waist -3.233e+02 7.726e+01 -4.185 0.00236 **
m_a2 1.895e+00 8.199e-01 2.312 0.04610 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.378 on 9 degrees of freedom
Multiple R-squared: 0.8949, Adjusted R-squared: 0.7782
F-statistic: 7.667 on 10 and 9 DF, p-value: 0.002621
|
Call:
lm(formula = y ~ SWV + density + m_taper + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.3691 -2.2349 -0.4538 1.6654 16.0980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.501185 7.972952 6.083 1.94e-08 ***
SWV -0.006148 0.001506 -4.083 8.70e-05 ***
density -0.014984 0.005031 -2.978 0.0036 **
m_taper -0.222586 0.131070 -1.698 0.0924 .
m_whorliness -2.041574 1.258311 -1.622 0.1077
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.451 on 105 degrees of freedom
Multiple R-squared: 0.189, Adjusted R-squared: 0.1581
F-statistic: 6.119 on 4 and 105 DF, p-value: 0.0001824
|
Call:
lm(formula = y ~ SWV + m_led + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-5.4833 -1.2486 -0.4416 1.9113 4.4711
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 86.50366 19.50337 4.435 0.000363 ***
SWV -0.01948 0.00460 -4.236 0.000557 ***
m_led -0.03015 0.01243 -2.426 0.026685 *
m_a1 -0.04164 0.02580 -1.614 0.125005
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.824 on 17 degrees of freedom
Multiple R-squared: 0.5505, Adjusted R-squared: 0.4711
F-statistic: 6.939 on 3 and 17 DF, p-value: 0.002966
|
Call:
lm(formula = y ~ SWV + m_ovality + density + m_a1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.8071 -3.4889 -0.9923 2.6980 17.9881
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.163e+01 1.133e+01 4.559 1.4e-05 ***
SWV -7.409e-03 2.182e-03 -3.395 0.00097 ***
m_ovality 1.994e+03 7.068e+02 2.821 0.00573 **
density -1.666e-02 7.329e-03 -2.273 0.02507 *
m_a1 -3.115e-02 1.721e-02 -1.810 0.07311 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.11 on 105 degrees of freedom
Multiple R-squared: 0.1666, Adjusted R-squared: 0.1349
F-statistic: 5.249 on 4 and 105 DF, p-value: 0.0006807
|
Call:
lm(formula = y ~ SWV + m_ovality + m_whorliness + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.5884 -3.6210 0.4863 2.8844 9.1941
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.624e+01 2.236e+01 3.410 0.00359 **
SWV -2.094e-02 5.895e-03 -3.552 0.00265 **
m_ovality 2.646e+03 1.412e+03 1.874 0.07936 .
m_whorliness 9.132e+00 5.657e+00 1.614 0.12601
m_taper -5.635e-01 3.774e-01 -1.493 0.15493
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.448 on 16 degrees of freedom
Multiple R-squared: 0.5543, Adjusted R-squared: 0.4429
F-statistic: 4.975 on 4 and 16 DF, p-value: 0.00846
|
Call:
lm(formula = y ~ SWV + density + m_taper, data = LL)
Residuals:
Min 1Q Median 3Q Max
-9.8770 -2.9641 -0.8829 2.2955 29.8845
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.793153 14.951333 5.136 1.56e-06 ***
SWV -0.011094 0.002821 -3.933 0.000163 ***
density -0.027576 0.009940 -2.774 0.006698 **
m_taper -0.484845 0.215945 -2.245 0.027149 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.003 on 92 degrees of freedom
Multiple R-squared: 0.1883, Adjusted R-squared: 0.1618
F-statistic: 7.112 on 3 and 92 DF, p-value: 0.000238
|
Call:
lm(formula = y ~ SWV + m_a0 + hw.vfrac + m_whorliness, data = LL)
Residuals:
Min 1Q Median 3Q Max
-8.4976 -3.4642 0.1459 3.1434 15.1500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 135.928624 33.825081 4.019 0.00112 **
SWV -0.025060 0.008128 -3.083 0.00757 **
m_a0 -0.083429 0.024781 -3.367 0.00424 **
hw.vfrac -38.914741 22.744306 -1.711 0.10768
m_whorliness -9.543790 6.440547 -1.482 0.15909
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.898 on 15 degrees of freedom
Multiple R-squared: 0.5262, Adjusted R-squared: 0.3998
F-statistic: 4.164 on 4 and 15 DF, p-value: 0.01826
|
Call:
lm(formula = y ~ SWV + m_led + density, data = LL)
Residuals:
Min 1Q Median 3Q Max
-3.248 -1.267 0.010 1.006 4.360
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.7109424 3.6064887 3.802 0.00024 ***
SWV -0.0038241 0.0006847 -5.585 1.82e-07 ***
m_led -0.0139476 0.0022681 -6.150 1.40e-08 ***
density 0.0073719 0.0023770 3.101 0.00247 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.64 on 106 degrees of freedom
Multiple R-squared: 0.3886, Adjusted R-squared: 0.3713
F-statistic: 22.46 on 3 and 106 DF, p-value: 2.469e-11
|
Call:
lm(formula = y ~ SWV + m_led + m_whorliness + hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.8945 -0.5266 -0.1464 0.9082 1.8528
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.459560 6.808582 4.474 0.000384 ***
SWV -0.005985 0.001618 -3.698 0.001949 **
m_led -0.014924 0.004404 -3.389 0.003745 **
m_whorliness -2.370611 1.210732 -1.958 0.067903 .
hw.vfrac -5.814268 4.201633 -1.384 0.185418
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.145 on 16 degrees of freedom
Multiple R-squared: 0.5512, Adjusted R-squared: 0.439
F-statistic: 4.913 on 4 and 16 DF, p-value: 0.008902
|
Call:
lm(formula = y ~ SWV + m_led + density + m_sed + m_sweep1, data = LL)
Residuals:
Min 1Q Median 3Q Max
-4.582 -1.796 -0.130 1.532 5.547
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.7874359 5.2124739 2.453 0.015820 *
SWV -0.0039318 0.0009836 -3.997 0.000120 ***
m_led -0.0519144 0.0141102 -3.679 0.000373 ***
density 0.0138862 0.0033889 4.098 8.29e-05 ***
m_sed 0.0363740 0.0152884 2.379 0.019175 *
m_sweep1 -0.5420807 0.3583244 -1.513 0.133358
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.327 on 104 degrees of freedom
Multiple R-squared: 0.393, Adjusted R-squared: 0.3638
F-statistic: 13.47 on 5 and 104 DF, p-value: 3.999e-10
|
Call:
lm(formula = y ~ SWV + m_led + m_whorliness + m_taper + m_sed +
m_a1 + m_a2 + hw.vfrac, data = LL)
Residuals:
Min 1Q Median 3Q Max
-1.2775 -0.5499 -0.2484 0.8138 1.9334
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.475169 11.009177 3.676 0.00317 **
SWV -0.008303 0.002532 -3.278 0.00660 **
m_led 0.072152 0.043742 1.650 0.12496
m_whorliness -2.805539 1.246491 -2.251 0.04394 *
m_taper -6.822035 2.695447 -2.531 0.02637 *
m_sed -0.084517 0.039825 -2.122 0.05532 .
m_a1 1.325251 0.567780 2.334 0.03779 *
m_a2 1.302610 0.567042 2.297 0.04040 *
hw.vfrac -7.669701 4.630325 -1.656 0.12353
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.171 on 12 degrees of freedom
Multiple R-squared: 0.8209, Adjusted R-squared: 0.7015
F-statistic: 6.876 on 8 and 12 DF, p-value: 0.001684
|
3 Conclusions
Best off using a dynamic estimate of board stiffness based on manual hitman velocity and CHH total density, but other measures of board stiffness (including Metriguard CLT) give similar results.
Green log SWV alone very poorly predicts average dry board stiffness.
Distance from pith at LE has limitations as a predictor for average distance from the pith throughout a board.
The saw pattern plots above indicate that there are a lot of boards missing from the dataset. While in routine operation this is ineveitable (and hence an unaviodable if log performance is to be predicted) in a research context this adds extra confusion owing to the boards being missing to different degrees in different logs and the missing boards being clustered (rather than randomly distributed) within individual logs.
4 Todo
- other approaches to finding ‘best’ model. DONE
- models for ‘good’ subsets. DONE
- models for inner/outer boards. DONE